{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Mushroom Classification\n",
    "In this notebook, I will make use of the Mushroom Classification dataset to try to predict if a Mushroom is poisonous or not by looking at the given features. I will successivelly try differerent feature elimination techniques to see how this can affect training times and overall model accuracy."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Reducing the number of features in a dataset, can lead to:\n",
    "- Accuracy improvements\n",
    "- Overfitting risk reduction\n",
    "- Speed up in training\n",
    "- Improved Data Visualization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Preprocessing"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# 前两行画图\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 数据处理\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 系统库\n",
    "import os, sys\n",
    "\n",
    "# 自带数据\n",
    "datalib_path = os.path.join(os.path.abspath('.'), '../')\n",
    "sys.path.append(datalib_path)\n",
    "import dataset\n",
    "\n",
    "# seaborn 画图库\n",
    "import seaborn as sns"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "from matplotlib.pyplot import figure\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import time\n",
    "import os"
   ],
   "outputs": [],
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "# 读取数据\n",
    "df = pd.read_csv(os.path.join(dataset.mushrooms_path, 'mushrooms.csv'))\n",
    "pd.options.display.max_columns = None\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
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       "      <th>veil-color</th>\n",
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       "      <td>p</td>\n",
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      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color stalk-shape stalk-root  \\\n",
       "0            c         n          k           e          e   \n",
       "1            c         b          k           e          c   \n",
       "2            c         b          n           e          c   \n",
       "3            c         n          n           e          e   \n",
       "4            w         b          k           t          e   \n",
       "\n",
       "  stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring  \\\n",
       "0                        s                        s                      w   \n",
       "1                        s                        s                      w   \n",
       "2                        s                        s                      w   \n",
       "3                        s                        s                      w   \n",
       "4                        s                        s                      w   \n",
       "\n",
       "  stalk-color-below-ring veil-type veil-color ring-number ring-type  \\\n",
       "0                      w         p          w           o         p   \n",
       "1                      w         p          w           o         p   \n",
       "2                      w         p          w           o         p   \n",
       "3                      w         p          w           o         p   \n",
       "4                      w         p          w           o         e   \n",
       "\n",
       "  spore-print-color population habitat  \n",
       "0                 k          s       u  \n",
       "1                 n          n       g  \n",
       "2                 n          n       m  \n",
       "3                 k          s       u  \n",
       "4                 n          a       g  "
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'percent_missing': percent_missing})\n",
    "missing_values.sort_values(by ='percent_missing' , ascending=False)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th></th>\n",
       "      <th>percent_missing</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>veil-color</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>stalk-shape</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>gill-color</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>gill-size</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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       "      <td>gill-spacing</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>gill-attachment</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>odor</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>bruises</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cap-color</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cap-surface</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>habitat</td>\n",
       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                          percent_missing\n",
       "class                                 0.0\n",
       "stalk-surface-above-ring              0.0\n",
       "population                            0.0\n",
       "spore-print-color                     0.0\n",
       "ring-type                             0.0\n",
       "ring-number                           0.0\n",
       "veil-color                            0.0\n",
       "veil-type                             0.0\n",
       "stalk-color-below-ring                0.0\n",
       "stalk-color-above-ring                0.0\n",
       "stalk-surface-below-ring              0.0\n",
       "stalk-root                            0.0\n",
       "cap-shape                             0.0\n",
       "stalk-shape                           0.0\n",
       "gill-color                            0.0\n",
       "gill-size                             0.0\n",
       "gill-spacing                          0.0\n",
       "gill-attachment                       0.0\n",
       "odor                                  0.0\n",
       "bruises                               0.0\n",
       "cap-color                             0.0\n",
       "cap-surface                           0.0\n",
       "habitat                               0.0"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "sns.set(style=\"ticks\")\n",
    "f = sns.countplot(x=\"class\", data=df, palette=\"bwr\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "df['class'].value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "Name: class, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "df.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(8124, 23)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can now divide our dataset in features (X) and labels (Y). We then transoform all our dataset categorical features in numeric features using One Hot Encoding. <br> <br>\n",
    "A new column gets created for all the diffent cases in a categorical feature. <br> <br>\n",
    "For example, the **bruises** feature contains two categorical cases **f** and **t**. This categorical feature will be splitted in two numeric features, one having 1s in all the rows for which the mushroom which had bruises f (**bruises_f**) and the second one having 1s for all the  rows for which the mushroom had bruises t (**bruises_t**). <br> <br>\n",
    "In the case of our labels (Y), we instead encode them. In our example, we have two different possible outcomes (edible or not). Therefore, we set the color of the first outcome equal to 0 and the second possible outcome equal to 1. And all the informations for this class will be contained in a single array (column). <br> <br>\n",
    "\n",
    "I decided not to adopt this same approch for the features (X), because some Machine Learning classifier might think that higher numbers are more important than lower ones and therefore would not give the same importance to the all the different categories in the feature (this doesn't instead happen when we encode the labels)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "X = df.drop(['class'], axis = 1)\n",
    "Y = df['class']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "X = pd.get_dummies(X, prefix_sep='_')\n",
    "X.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
       "0            1              0              0              1              0   \n",
       "1            1              0              0              1              0   \n",
       "2            0              0              0              1              0   \n",
       "3            1              0              0              0              1   \n",
       "4            1              0              0              1              0   \n",
       "\n",
       "   cap-color_b  cap-color_c  cap-color_e  cap-color_g  cap-color_n  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            1            0   \n",
       "\n",
       "   cap-color_p  cap-color_r  cap-color_u  cap-color_w  cap-color_y  bruises_f  \\\n",
       "0            0            0            0            0            0          0   \n",
       "1            0            0            0            0            1          0   \n",
       "2            0            0            0            1            0          0   \n",
       "3            0            0            0            1            0          0   \n",
       "4            0            0            0            0            0          1   \n",
       "\n",
       "   bruises_t  odor_a  odor_c  odor_f  odor_l  odor_m  odor_n  odor_p  odor_s  \\\n",
       "0          1       0       0       0       0       0       0       1       0   \n",
       "1          1       1       0       0       0       0       0       0       0   \n",
       "2          1       0       0       0       1       0       0       0       0   \n",
       "3          1       0       0       0       0       0       0       1       0   \n",
       "4          0       0       0       0       0       0       1       0       0   \n",
       "\n",
       "   odor_y  gill-attachment_a  gill-attachment_f  gill-spacing_c  \\\n",
       "0       0                  0                  1               1   \n",
       "1       0                  0                  1               1   \n",
       "2       0                  0                  1               1   \n",
       "3       0                  0                  1               1   \n",
       "4       0                  0                  1               0   \n",
       "\n",
       "   gill-spacing_w  gill-size_b  gill-size_n  gill-color_b  gill-color_e  \\\n",
       "0               0            0            1             0             0   \n",
       "1               0            1            0             0             0   \n",
       "2               0            1            0             0             0   \n",
       "3               0            0            1             0             0   \n",
       "4               1            1            0             0             0   \n",
       "\n",
       "   gill-color_g  gill-color_h  gill-color_k  gill-color_n  gill-color_o  \\\n",
       "0             0             0             1             0             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             0             1             0   \n",
       "3             0             0             0             1             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   gill-color_p  gill-color_r  gill-color_u  gill-color_w  gill-color_y  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   stalk-shape_e  stalk-shape_t  stalk-root_?  stalk-root_b  stalk-root_c  \\\n",
       "0              1              0             0             0             0   \n",
       "1              1              0             0             0             1   \n",
       "2              1              0             0             0             1   \n",
       "3              1              0             0             0             0   \n",
       "4              0              1             0             0             0   \n",
       "\n",
       "   stalk-root_e  stalk-root_r  stalk-surface-above-ring_f  \\\n",
       "0             1             0                           0   \n",
       "1             0             0                           0   \n",
       "2             0             0                           0   \n",
       "3             1             0                           0   \n",
       "4             1             0                           0   \n",
       "\n",
       "   stalk-surface-above-ring_k  stalk-surface-above-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-above-ring_y  stalk-surface-below-ring_f  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   stalk-surface-below-ring_k  stalk-surface-below-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-below-ring_y  stalk-color-above-ring_b  \\\n",
       "0                           0                         0   \n",
       "1                           0                         0   \n",
       "2                           0                         0   \n",
       "3                           0                         0   \n",
       "4                           0                         0   \n",
       "\n",
       "   stalk-color-above-ring_c  stalk-color-above-ring_e  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_g  stalk-color-above-ring_n  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_o  stalk-color-above-ring_p  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_w  stalk-color-above-ring_y  \\\n",
       "0                         1                         0   \n",
       "1                         1                         0   \n",
       "2                         1                         0   \n",
       "3                         1                         0   \n",
       "4                         1                         0   \n",
       "\n",
       "   stalk-color-below-ring_b  stalk-color-below-ring_c  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_e  stalk-color-below-ring_g  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_n  stalk-color-below-ring_o  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_p  stalk-color-below-ring_w  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   stalk-color-below-ring_y  veil-type_p  veil-color_n  veil-color_o  \\\n",
       "0                         0            1             0             0   \n",
       "1                         0            1             0             0   \n",
       "2                         0            1             0             0   \n",
       "3                         0            1             0             0   \n",
       "4                         0            1             0             0   \n",
       "\n",
       "   veil-color_w  veil-color_y  ring-number_n  ring-number_o  ring-number_t  \\\n",
       "0             1             0              0              1              0   \n",
       "1             1             0              0              1              0   \n",
       "2             1             0              0              1              0   \n",
       "3             1             0              0              1              0   \n",
       "4             1             0              0              1              0   \n",
       "\n",
       "   ring-type_e  ring-type_f  ring-type_l  ring-type_n  ring-type_p  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            1   \n",
       "2            0            0            0            0            1   \n",
       "3            0            0            0            0            1   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   spore-print-color_b  spore-print-color_h  spore-print-color_k  \\\n",
       "0                    0                    0                    1   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    1   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   spore-print-color_n  spore-print-color_o  spore-print-color_r  \\\n",
       "0                    0                    0                    0   \n",
       "1                    1                    0                    0   \n",
       "2                    1                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   spore-print-color_u  spore-print-color_w  spore-print-color_y  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   population_a  population_c  population_n  population_s  population_v  \\\n",
       "0             0             0             0             1             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             1             0             0   \n",
       "3             0             0             0             1             0   \n",
       "4             1             0             0             0             0   \n",
       "\n",
       "   population_y  habitat_d  habitat_g  habitat_l  habitat_m  habitat_p  \\\n",
       "0             0          0          0          0          0          0   \n",
       "1             0          0          1          0          0          0   \n",
       "2             0          0          0          0          1          0   \n",
       "3             0          0          0          0          0          0   \n",
       "4             0          0          1          0          0          0   \n",
       "\n",
       "   habitat_u  habitat_w  \n",
       "0          1          0  \n",
       "1          0          0  \n",
       "2          0          0  \n",
       "3          1          0  \n",
       "4          0          0  "
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "len(X.columns)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "Y = LabelEncoder().fit_transform(Y)\n",
    "#np.set_printoptions(threshold=np.inf)\n",
    "Y"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Machine Learning"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X2 = StandardScaler().fit_transform(X)\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X2, Y, test_size = 0.30, random_state = 101)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "start = time.process_time()\n",
    "trainedmodel = LogisticRegression().fit(X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictions =trainedmodel.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictions))\n",
    "print(classification_report(Y_Test,predictions))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.1725089999999998\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/guchen/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "start = time.process_time()\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionsvm = trainedsvm.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionsvm))\n",
    "print(classification_report(Y_Test,predictionsvm))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.2655290000000008\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/guchen/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "start = time.process_time()\n",
    "trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionstree = trainedtree.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionstree))\n",
    "print(classification_report(Y_Test,predictionstree))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.03340799999999966\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "import graphviz\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "\n",
    "data = export_graphviz(trainedtree,out_file=None,feature_names= X.columns,\n",
    "                       class_names=['edible', 'poisonous'],  \n",
    "                       filled=True, rounded=True,  \n",
    "                       max_depth=2,\n",
    "                       special_characters=True)\n",
    "graph = graphviz.Source(data)\n",
    "graph"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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x=\"215.54\" y=\"-106.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 382</text>\n<text text-anchor=\"start\" x=\"209.3\" y=\"-92.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [358, 24]</text>\n<text text-anchor=\"start\" x=\"218.65\" y=\"-78.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = edible</text>\n</g>\n<!-- 1&#45;&gt;9 -->\n<g id=\"edge5\" class=\"edge\">\n<title>1&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M261.26,-185.77C261.26,-177.57 261.26,-168.8 261.26,-160.28\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"264.76,-160.06 261.26,-150.06 257.76,-160.06 264.76,-160.06\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#c0c0c0\" stroke=\"black\" d=\"M47.26,-36C47.26,-36 17.26,-36 17.26,-36 11.26,-36 5.26,-30 5.26,-24 5.26,-24 5.26,-12 5.26,-12 5.26,-6 11.26,0 17.26,0 17.26,0 47.26,0 47.26,0 53.26,0 59.26,-6 59.26,-12 59.26,-12 59.26,-24 59.26,-24 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      "text/plain": [
       "<graphviz.files.Source at 0x7ffa9bcec390>"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Feature Selection"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "There are many different methods which can be applied for Feature Selection. Some of the most important ones are: <br>\n",
    "1. Filter Method = filtering our dataset and taking only a subset of it containg all the relevant features (eg. correlation matrix using Pearson Correlation)\n",
    "2. Wrapper Method = follows the same objective of the FIlter Method but uses a Machine Learning model as it's evaluation criteria (eg. Forward/Backward/Bidirectional/Recursive Feature Elimination). We feed some features to our Machine Learning model, evaluate their performance and then decide if add or remove feature to increase accuracy. As a result, this mothod can be more accurate than filtering, is more computationally expensive.\n",
    "3. Embedded Method = like the FIlter Method also the Embedded Method makes use of a Machine Learning model. The difference between the two different methods is that the Embedded Method examines the different training iterations of our ML model and then ranks the importance of each feature based on how much each of the features contributed to the ML model training (eg. LASSO Regularization)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Feature Importance"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Decision Trees models which are based on ensembles (eg. Extra Trees and Random Forest) can be used to rank the importaqnce of the different features. Knowing which features our model is giving most importance can be of vital importance to understand how our model is making it's predictions (therefore making it more explainable). At the same time, we can get rid of the features which do not bring any benefit to our model (our confuse it to make a wrong decision!)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "2.471509000000001\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "figure(num=None, figsize=(20, 22), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "feat_importances = pd.Series(trainedforest.feature_importances_, index= X.columns)\n",
    "feat_importances.nlargest(19).plot(kind='barh')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa92178eb8>"
      ]
     },
     "metadata": {},
     "execution_count": 30
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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3Z5xxRk499dQkyVZbbZVx48alVatW65xlm222yWmnnZa33noru+22W0aMGPEP3VtVVVWuvvrqfPjhhznuuOPW2Hf66adnzJgx6dOnT+rr63PAAQfk+OOPT11dXX7+85+nb9++qaury4477phf/OIXa7y2efPmufHGGzNq1KjcdtttWbVqVY488sgcc8wxGzzj/vvvn2XLlqVnz55JksrKytxyyy3p0aPH579xAAAAAGCjK2lY15oEX3KLFy/OoYcemhdeeGGTXfP666/PkiVLMmrUqE12zS3NwoUL069fv+xcdXZKWn29qcdhE2tZXpbfjO7Z1GMAAAAAfGl93NemT5+eioqKtfZ/6Z8M5ot11lln5eWXX17nvvPOOy8HHHDAJp4IAAAAAPg8tsgY3LFjx036VHCSnHnmmZv0epvKNddc09QjAAAAAAAbwZf+C+QAAAAAAPhsYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAZU09AHxSi/LSNCv30SyaVi38zgEAAAC+SOoLm50Rp3RNRUVFU49BE1i1uj7Ny/zBAgAAAMAXQXUBNhtCMAAAAMAXR3kBAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRjYLK1aXd/UIwAAAABsUcqaegD4pJ+OfzzNWr3S1GPQhFq1KMsdl/Ro6jEAAAAAtihiMJudDz6sS0np6qYeAwAAAAC2KJaJAAAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMbkIXX3xxnnnmmc88rqqqKk8//XSSZNddd82SJUu+6NEa3XDDDbnnnns22fUAAAAAgC9GWVMPUGSPPfZYevfu3dRj/F1PPPFEOnbsuFHPuXTp0rz++utrba+trd2o1wEAAAAA/n9i8CawfPnynHfeeVmyZElKSkpSUVGRtm3bZunSpbngggsycuTIfPOb38yll16a9957L0uXLk3Hjh1zzTXXpH379us857JlyzJ48OB07949Z5555lr7q6qqsueee+aPf/xjTj755FRWVmbkyJFZtGhRSkpK0r179/zbv/1bmjdvntra2lx22WV54403kiR9+vTJySefnDvuuCPPP/98Fi9enJKSkhx11FGfeo8rVqzImDFj8uKLL2bVqlXZc889c/7556dNmzZrHTt16tSMHTv2c76bAAAAAMDnYZmITWDmzJlp0aJFqqurM23atDQ0NKR///7p0KFDRo8enS5duuSuu+7KYYcdlilTpmTu3LkpLS1NdXX1Os/35ptv5oc//GH69u27zhD8sR122CH33HNPjj766Jx77rnZfffdc/fdd+euu+7KwoULM378+NTV1WXIkCHp3bt3Zs2alUmTJqW6ujo1NTUZNGhQvv3tb+ess876uyE4ScaMGZNvfetbmT59embOnJk2bdrkqquuWuexAwYMyPTp09f6ufLKK9f/TQUAAAAANogngzeBrl27Zty4cRk0aFC6dOmSQYMGpVOnTmscc9ZZZ+Xxxx/PbbfdlkWLFqW2tjb77rvvOs83ZMiQtGnTJsccc8zfve5+++2XJHn//ffz9NNP56abbkqStGjRIscff3xuueWW9OjRI++880769OmTJGnXrl2OPvroPPzwwxu0hMVDDz2UZ599NjNnzkySrFq1Kl/96lfXeWyHDh3SoUOH9T43AAAAAPCPE4M3gZ122ikPPvhgFixYkAULFuTEE0/M8OHD1zjm3HPPzfvvv5+ePXvmgAMOyMqVK9PQ0LDO811wwQWZMmVKrrvuupxzzjl57rnnctFFFzXur6mpSfJR9E2ShoaGtc5VX1+furq61NfXr3X+j/dtiPr6+vz85z/P7rvvnuSjZSNWrVq1QecAAAAAAL44lonYBG655ZaMHDky3bt3z7Bhw1JZWZmXXnoppaWljdH1kUceyZAhQ9KrV6+0bds2jz/++KcG2X322SdjxozJ1KlT89RTT2WPPfZITU1N488ntW7dOvvuu28mTJiQJPnggw8yZcqUVFZWZocddsjWW2/duCTF8uXLM2PGjFRWVibJGjP+PQceeGAmTJiQurq61NXVZfjw4fnFL37xud4vAAAAAGDjE4M3gQEDBuTtt9/OEUcckX79+uWdd97JSSedlEMOOSTDhg3L3Llzc+655+acc85Jv379cs4556Rz585ZtGjRp57zG9/4RoYPH55hw4bl3Xff/cwZfv7zn2fhwoXp1atXevfunU6dOuWMM85I8+bNc+ONN6a6ujq9evXKgAED0qNHj8YlKKqqqnL11Vdn8uTJf/f8Hz+ZfNRRR6Vnz55p2bJlzjvvvA14lwAAAACAL1JJw6etRQCb2MKFC9OvX7/sXHV2Slp9vanHoQm1LC/Lb0b3bOoxAAAAAL5UPu5r06dPT0VFxVr7rRnMern11lsza9asde7r2bNnTj311E08EQAAAACwIcRg1svgwYMzePDgph4DAAAAAPicrBkMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUQFlTDwCf1KK8NM3KfTSLrFULv38AAACAjU1xYbMz4pSuqaioaOoxaGKrVteneZk/XgAAAADYWJQWYLMkBAMAAABsXGoLAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMFAIa1aXd/UIwAAAABsUmVNPQB80k/HP55mrV5p6jHYgrVqUZY7LunR1GMAAAAAbFJiMJudDz6sS0np6qYeAwAAAAC2KJaJAAAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgzeS5557LkOGDEmSXH/99bnwwguTJAMHDkxNTU1TjpbevXvnzTffbNIZAAAAAICmVdbUA2wp9thjj4wbN66px1inpo7RAAAAAEDTE4M/h9tvvz1TpkxJ69at06VLl0yYMCG//OUvc9FFF+WBBx5Yr3M0NDTkiiuuyPz589O8efP80z/9U0aPHp1tt902VVVVqaqqyrPPPpu33347Rx99dE4//fQkSXV1de68886sXr06b775Zvr3758zzzyzcd+tt96akpKStG7dOiNHjswuu+ySXXfdNfPmzcuiRYtyxRVXZJdddslLL72UlStX5qKLLkplZWU++OCDjBw5Mk899VTatm2bvfbaK//93/+diRMnfuo9PPjgg7nhhhsyffr0JMmxxx6bHXfcMWPGjMmqVatSWVmZu+++O9tss80ar1u6dGlef/31tc5XW1u7Xu8dAAAAALDhxOAN9Pvf/z6TJk3KtGnT0r59+1xzzTWpq6vb4PP88Y9/zKxZszJv3ryUlpbm9ttvz3/8x3+kR48eSZIVK1bkrrvuyrJly9K/f/9UVFSkc+fOufPOO3PDDTdkm222yWuvvZaDDz44J5xwQt54442MHj06v/3tb7Pddtuluro6V1999VpPKy9cuDA//elPs+eee+ZXv/pVrrnmmlRWVuamm27KsmXLcu+996a+vj4/+tGPPvMeKisrM2zYsLz++utp3rx5XnvttfzlL39JkixYsCC77LLLWiE4SaZOnZqxY8du8HsGAAAAAHx+YvAGeuihh9KjR4+0b98+STJo0KDcdNNNG3yeHXbYIR06dEifPn1y4IEH5sADD0zXrl0b9w8cODDNmjVL+/bt06NHjzz66KP5l3/5l4wfPz6/+93vsmjRotTW1qahoSErV67MY489li5dumS77bZLkvTp0yd9+vRZ67rbbLNN9txzzyTJ7rvvnjvuuCNJ8vDDD+fss89OWdlHH4njjjvu7z4VnCTl5eXp1q1bHnnkkWy11VY55JBD8uSTT+ZPf/pTHnjggcaw/UkDBgxIVVXVWttra2szdOjQz37zAAAAAIANJgZvoLKysjQ0NDT+v1mz9fsOvgcffDDXXXddkqRDhw4ZP358pk2blv/8z//MggULMnr06Oy777659NJLkySlpaWNr21oaEizZs2yZMmSDBgwIEcffXT22muvfO9738t9992XhoaGxoj7sdWrV6e2tja77rrrGttbtGjxqfdVX1+/wffVo0eP3HPPPSkvL8/hhx+e5s2bZ/78+Xn44Ycbl7b4pA4dOqRDhw7rdX4AAAAAYONYv+JHo4MOOij3339/3n777STJlClT1ut1Bx98cGpqalJTU5Px48fn6aefztFHH53ddtstQ4YMyaBBg/LCCy80Hv/xOrxvvfVW7rvvvlRVVeX5559P69atc8YZZ6R79+555JFH0tDQkPr6+nTp0iVPPfVUXnvttSTJnDlzcv7552/QfU2fPj2rV6/OqlWrMm3atPV6Xbdu3fLMM8/k6aefTufOnVNZWZkJEyZku+22E3wBAAAAYDPiyeAN1Llz55x00kn5/ve/n/Ly8uy+++5p2bLlBp9n3333TdeuXdOnT5+0bt06W221VS666KLG/UuXLk2/fv3y3nvv5eSTT07Xrl3zwQcfZMaMGenRo0dat26dnXbaKTvuuGNeeeWVdOvWLRdffHFOO+20NDQ0ZOutt85VV1213vOceuqpGTVqVI466qi0atUq22233RpPCn+aVq1aZe+9987y5cvTunXr7L///nnrrbdy0kknbfB7AgAAAAB8cUoa/nbNAz7Tc889lyeffDInn3xykuTee+/NhAkT1vsJ4fVRVVWVK664Ivvtt99GO+dnmT17dsrLy3PIIYckSUaMGJG2bdvm7LPP3mQzLFy4MP369cvOVWenpNXXN9l1KZ6W5WX5zeieTT0GAAAAwEb1cV+bPn16Kioq1trvyeANtOOOO+a2225Lz549U1pamnbt2mXUqFFNPdY/bJdddskll1yS6667LnV1damoqMg555yTW2+9NbNmzVrna3r27JlTTz11E08KAAAAAHweYvAGatOmTa655pov9BoPPfTQF3r+ddl5550zefLktbYPHjw4gwcP3uTzAAAAAAAbly+QAwAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAogLKmHgA+qUV5aZqV+2jyxWnVwucLAAAAKB5FhM3OiFO6pqKioqnHYAu3anV9mpf54wgAAACgOJQQoJCEYAAAAKBo1BAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgPbwzwoAACAASURBVAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwE2klWr65t6BAAAAIBPVdbUA8An/XT842nW6pWmHgM2SKsWZbnjkh5NPQYAAADApxKD2ex88GFdSkpXN/UYAAAAALBFsUwEAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFMDnjsEXX3xxnnnmmc88rqqqKk8//XSSZNddd82SJUs+7yU3yEsvvZRDDjkkffv2zZ/+9KdNcs11GT58eG688cYmu/7fuvDCCzNv3rymHgMAAAAAaAKfOwY/9thjqa+v35izbFRz587N3nvvnRkzZmTnnXdu6nE2C6NGjUr37t2begwAAAAAoAmUfdYBy5cvz3nnnZclS5akpKQkFRUVadu2bZYuXZoLLrggI0eOzDe/+c1ceumlee+997J06dJ07Ngx11xzTdq3b7/Ocy5btiyDBw9O9+7dc+aZZ661/9Zbb01NTU222mqrlJeXZ8SIEdl5550zcODA9O/fP717906SDBo0KEcddVT69euXXXfdNb169crzzz+fIUOGZPLkyVm9enVOP/30jB07NldccUWefvrpfPDBB1m5cmWGDx+eQw45JHV1dbnqqqvy4IMPpqysLJ06dcro0aPTpk2bzJgxIxMnTkx9fX1at26dYcOGZa+99lpr3g8++CAjRoxIbW1t3n333TRr1iyXXXZZ9tlnnyTJH/7whwwYMCArVqzInnvumYsuuiitW7dObW1tLrvssrzxxhtJkj59+uTkk0/OXXfdldmzZ+eOO+5Ikrz55ps59NBD89BDD+XDDz/MZZddlsWLF2fVqlWprKzMOeeck7KytX+Vf/ueXHLJJbnpppsa379dd901Z511VubNm5elS5fm2GOPzamnnpokuf322zNlypS0bt06Xbp0yYQJE/LCCy/83c/JwoULM3LkyKxatSoNDQ3p379/TjjhhHUeu3Tp0rz++utrba+trf271wAAAAAAPr/PfDJ45syZadGiRaqrqzNt2rTG0NehQ4eMHj06Xbp0yV133ZXDDjssU6ZMydy5c1NaWprq6up1nu/NN9/MD3/4w/Tt23edIXjFihW55pprMnny5Pz2t7/NMccckyeffHK9bqZz586ZM2dO+vTpk2OPPTaHHHJIbrjhhjz33HN55ZVXMnny5MyaNStnnHFGrr322iTJlClT8u///u+ZMWNGZs+ena985Su588478/TTT2fKlCmZOHFiqqurc8EFF+S0007LypUr17ru/PnzU1ZWlqlTp+bee+/NEUcckZtvvrlx/2uvvZbbbrstM2fOzHvvvZebbropdXV1GTJkSHr37p1Zs2Zl0qRJqa6uTk1NTXr27JmFCxfmz3/+c5Kkuro6hxxySNq1a5dhw4bliCOOyPTp0zN9+vT8+c9/bozGf+89OeCAA9bat9VWW2XKlCm58cYbc+211+bdd9/N73//+0yaNClTpkzJjBkzUl5enrq6us9878eNG5fevXtn+vTpueWWW/Lkk09m9erV6zx26tSp6dev31o/Q4cO/czrAAAAAACfz2c+Gdy1a9eMGzcugwYNSpcuXTJo0KB06tRpjWPOOuusPP7447ntttuyaNGi1NbWZt99913n+YYMGZI2bdrkmGOOWef+Nm3a5Lvf/W769u2b7t27p2vXrjnyyCPX62b222+/dW7fc889M3z48EybNi2vvvpqnn322bz33ntJkkcffTS9evVKq1atkiQjR45MklxxxRVZvHhxjj/++MbzNGvWLIsWLcpuu+22xvkPOeSQdOzYMXfeeWf+93//NwsWLEjbtm0b9/fu3Ttt2rRJkvTr1y/XXntt+vbtm3feeSd9+vRJkrRr1y5HH310Hn744fTu3Ts9e/bMjBkzcsYZZ2T69OkZOXJk3n///TzxxBN5++23c9NNNyX56Knk8vLyDX5PkuTggw9Okuy2226pr6/PO++8k4ceeig9evRofKp70KBBjdf6e3r06JERI0bkiSeeSJcuXXLxxRev82nlJBkwYECqqqrW2l5bWysIAwAAAMAX5DNj8E477ZQHH3wwCxYsyIIFC3LiiSdm+PDhaxxz7rnn5v3330/Pnj1zwAEHZOXKlWloaFjn+S644IJMmTIl1113Xc4555w899xzueiiixr319TU5Oabb86LL76YJ554IuPHj8+UKVMyfvz4JFnjvKtWrVrj3C1btlznNefNm5cRI0bkxBNPTLdu3bLPPvtk9OjRSZLmzZunpKSk8dhly5blvffeS319fQ4//PA1Znvttdey7bbb5pRTTsnSpUuTJD/5yU/y+uuvZ9KkSRk4cGAOPfTQ/L//9/8yZ86cxteVlpY2/ru+vj7Nmzdf53rL9fX1jU/hHnvssTn99NNTWVmZkpKS7LvvvlmxYkUaGhryq1/9Ku3atUvy0TIezZo1y4MPPpjrrrsuSdKhQ4fG9+vT3pMkadGiReO/S0pK0tDQkLKysjXe42bN1m9Z6SOPPDJdu3bN73//+zzxxBO5/vrr8+tf/zrf+ta31jq2Q4cO6dChw3qdFwAAAADYOD6z9N1yyy0ZOXJkunfvnmHDhqWysjIvvfRSSktLG8PlI488kiFDhqRXr15p27ZtHn/88U9dWmCfffbJmDFjMnXq1Dz11FPZY489UlNT0/jz6quvpqqqKttuu21OPPHEnHXWWY3r1bZv3z4vvvhikuTVV19t/PdnmT9/fiorKzNw4MDsu+++eeCBBxrn++53v5u77747H3zwQZLk6quvzsSJE3PggQdmzpw5+ctf/pIkuf/++9O3b998+OGHGT9+fOO8Bx98cB555JEcddRRGTBgQHbffffMnTt3jfu/++67s3LlyqxatSq/+c1v0q1bt+ywww7ZeuutG5fTWL58eWbMmJHKysokHz2t+7WvfS1jxozJgAEDknz01PR3vvOd3H777Uk+eir4lFNOyeTJk3PwwQc3zvRxCP48DjrooNx///15++23k3y0jMb6OOWUUzJv3rz06tUrl156adq2bZvFixd/7jkAAAAAgI3rM58MHjBgQIYPH54jjjgiLVq0yNe//vUMGzYsdXV1GTZsWC6++OKce+65Oeecc9KuXbs0b948nTt3zqJFiz71nN/4xjcyfPjwDBs2LDNnzlxjSYXtttsu3//+93PCCSekZcuWadasWeNTvD/60Y9y/vnn58gjj8z222+fLl26rNdNHnfccTn33HNz5JFHprS0NAcccECWLVuWd999N8ccc0yWLFmS/v37J/kowp5//vlp2bJlzjjjjMYvVdtqq60ybty4xuUk/tbgwYNz8cUXZ/bs2SkpKcl+++2X++67r/Hp3x133DE/+MEPsmLFinTu3DmnnnpqmjdvnhtvvDGjRo3KbbfdllWrVuXII49cY/mMAQMGZMSIEWvE3auuuiqXXXZZevXqlb/+9a/p1q1bTjrppPV6H9ZH586dc9JJJ+X73/9+ysvLs/vuu//dp4s/dtZZZ+XSSy/NHXfckdLS0hx00EGNYRsAAAAAaHolDZ+2ngOF9Nxzz+XJJ5/MySefnCS59957M2HChPV+QvgfsXDhwvTr1y87V52dklZf/8KvBxtTy/Ky/GZ0z6YeAwAAACiwj/va9OnTU1FRsdb+z3wymGLZcccdc9ttt6Vnz54pLS1Nu3btMmrUqDzxxBMZM2bMOl+z/fbbN65XDAAAAABsnsRg1tCmTZtcc801a23v1KlTampqmmAiAAAAAGBj+MwvkAMAAAAA4MtPDAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAAqgrKkHgE9qUV6aZuU+mny5tGrhMwsAAABs3tQLNjsjTumaioqKph4DNtiq1fVpXuYPLgAAAIDNk2oBsJEIwQAAAMDmTLkAAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRhgI1m1ur6pRwAAAAD4VGVNPQB80k/HP55mrV5p6jFgg7RqUZY7LunR1GMAAAAAfCoxmM3OBx/WpaR0dVOPAQAAAABbFMtEAAAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwV+A5557LkOGDEmSXH/99bnwwguTJAMHDkxNTU1TjpbevXvnzTffbNIZAAAAAIBNr6ypB9gS7bHHHhk3blxTj7FOTR2jAQD+P/buPbjq+s7/+CsXQ0hgtU5BO6tovdE1I6yrjIIoK9qllhUQtUgtHa0UrVbHQUFqI1QUdcRtUWitVVssHcAFI+BQvFAv660UW4uKrVuCYllkpVSYXggSkt8f/TUrgnI34PfxmGHGfM/38/m+zyE4k+d8cw4AANA6xOCd9MMf/jDTp09PdXV1TjzxxNx333350Y9+lNra2jz22GPbtEdzc3NuvfXWPPPMM9lnn33yiU98IjfddFMOOOCA9O7dO717986LL76Yd955J2effXYuu+yyJMmsWbMyderUNDY2ZvXq1TnnnHNy+eWXtzx2zz33pKSkJNXV1Rk7dmyOOuqodO7cOU899VSWLVuWW2+9NUcddVR++9vfZt26damtrU3Pnj3T0NCQsWPHZuHChWnfvn26du2aJUuWZMqUKR/6PFavXp3rr78+r732WkpLS9OnT59ceeWVm5339ttvZ9WqVZsdr6+v36bXCwAAAADYfmLwTnjuuefyk5/8JDNnzsz++++fCRMmZOPGjdu9z3//93/noYceylNPPZWysrL88Ic/zK9//ev06dMnSfLnP/85M2bMyJo1a3LOOeekpqYmJ5xwQqZOnZrvfve76dChQ1asWJHTTjst559/fv7whz/kpptuygMPPJCDDz44s2bNyre//e3N7lZevHhxxowZky5duuTHP/5xJkyYkJ49e+bOO+/MmjVrMm/evDQ1NeXiiy/epucxduzYdOzYMbfffnsaGhry5S9/OT179szxxx+/yXn3339/Jk2atN2vEwAAAACw48TgnfD444+nT58+2X///ZMkF1xwQe68887t3ufQQw9Nx44dM2DAgJx88sk5+eST071795bHhwwZktLS0uy///7p06dPnn766fzrv/5r7r777jz55JNZtmxZ6uvr09zcnHXr1uXZZ5/NiSeemIMPPjhJMmDAgAwYMGCz63bo0CFdunRJkhx99NGZPHlykuSJJ57I8OHDU17+t2+PwYMHb/Wu4CR5+umnM3369JSUlKRt27aZMWPGFs8bNGhQevfuvdnx+vr6jBgxYqvXAQAAAAC2nxi8E8rLy9Pc3NzydWnptn0e389+9rPccccdSZKOHTvm7rvvzsyZM7No0aIsWLAgN910U4477rh861vfSpKUlZW1rG1ubk5paWlWrlyZQYMG5eyzz07Xrl1z7rnn5pFHHklzc3NLxP27xsbG1NfXp3Pnzpscr6ys/MDn1dTUtN3Pa5999klJSUnL1//zP/+TqqqqfOITn9jkvI4dO6Zjx47btCcAAAAAsGtsW+Vji0499dQ8+uijeeedd5Ik06dP36Z1p512WmbPnp3Zs2fn7rvvzgsvvJCzzz47n/nMZ3LJJZfkggsuyKuvvtpyfl1dXZLkj3/8Yx555JH07t07r7zySqqrq/P1r389vXr1yn/913+lubk5TU1NOfHEE7Nw4cKsWLEiSfLwww/nG9/4xnY9r7q6ujQ2NmbDhg2ZOXPmNq076aSTWmZdv359Lr300rz44ovbfF0AAAAAYPdxZ/BOOOGEE/KVr3wlX/rSl9KmTZscffTRadu27Xbvc9xxx6V79+4ZMGBAqqurU1FRkdra2pbH33777QwcODB/+ctfctFFF6V79+5paGjIgw8+mD59+qS6ujpHHHFEPv3pT+eNN97IKaeckuuuuy6XXnppmpub8w//8A/5j//4j22eZ9iwYRk3blz69euXqqqqHHzwwZvcKfxBamtrc+ONN+bMM89MU1NT+vXrt8W3gwAAAAAAPnolze99nwO2y8svv5xf/OIXueiii5Ik8+bNy3333bfNdwhvi969e+fWW2/d7EPYdqe5c+emTZs2Of3005Mk119/fdq3b5/hw4fv1usuXrw4AwcOzJG9h6ek6sDdei3Y1dq2Kc9/3tS3tccAAAAACuzvfa2uri41NTWbPe7O4J3w6U9/Ovfee2/69u2bsrKy7Lvvvhk3blxrj7XTjjrqqIwePTp33HFHNm7cmJqamlx11VW555578tBDD21xTd++fTNs2LCPeFIAAAAAYFuJwTuhXbt2mTBhwm69xuOPP75b99+SI488MtOmTdvs+NChQzN06NCPfB4AAAAAYOf5ADkAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIob+0B4P0q25SltI1vTfYuVZW+ZwEAAIA9m3rBHuf6r3ZPTU1Na48B221DY1P2KfcLFwAAAMCeSbUA2EWEYAAAAGBPplwAAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAywh9vQ2NTaIwAAAAAfA+WtPQC835i7n09p1RutPQbsEaoqyzN5dJ/WHgMAAAD4GBCD2eM0rN+YkrLG1h4DAAAAAD5WvE0EAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYvIfp379/Vq9evcv2e+mll/KNb3xjl+23rUaNGpXvfe97H/l1AQAAAIAtK2/tAdjU7Nmzd+l+S5YsyVtvvbVL9wQAAAAA9j5icCtbsGBBxo4dm3333TfvvPNOli5dmqeeeirLli3LrbfemqOOOiq//e1vs27dutTW1qZnz55paGjI2LFjs3DhwrRv3z5du3bNkiVLMmXKlE32fvPNN3PHHXdk7dq1ufLKK7PffvuloqIi1157bZLk2WefzW233ZaJEyfmvPPOS69evbJ48eKsX78+V199dU477bQkyYMPPpgpU6akqakp1dXVGTlyZLp27brDz/ntt9/OqlWrNjteX1+/w3sCAAAAAB9ODN4DLFmyJI8++mgOOeSQdO7cueX44sWLM2bMmHTp0iU//vGPM2HChPTs2TN33nln1qxZk3nz5qWpqSkXX3zxFvft1KlTrrjiisyZMycTJkzIkiVLcv755+eqq65KmzZtMm3atHzxi19MkqxatSrHHntsxo0bl0WLFuXCCy/M/Pnzs3Tp0kyfPj1TpkxJdXV1Fi9enGHDhmX+/Plp27btDj3f+++/P5MmTdqhtQAAAADAjhGD9wAdOnTIIYccssXjXbp0SZIcffTRmTx5cpLkiSeeyPDhw1Ne/re/vsGDB292V/CWHHHEEencuXPmzZuX7t2754UXXsj48eOzevXqVFdX55xzzkmSdO3aNYceemgWLlyYRYsWZfny5S3ROElKS0uzbNmyfOYzn9mh5zto0KD07t17s+P19fUZMWLEDu0JAAAAAHw4MXgP8EF32FZWVm7xeHl5eZqamlq+Li39v88B7N+/f8t/33jjjZutHTJkSO6777689dZb6d+/f8u1/x6W36usrCxNTU0544wzUltb23J8xYoVOeCAA7byrD5Yx44d07Fjxx1eDwAAAABsv9Ktn8Ke5tRTT01dXV0aGxuzYcOGzJw5s+Wx2bNnt/w55phjUlZWlo0bN7Y83rt376xYsSIzZszI4MGDW46vXbs28+fPT5K8+OKLWb58ebp165aTTz45Dz/8cMuH0D366KM566yzsn79+o/o2QIAAAAAu4I7g/dCw4YNy7hx49KvX79UVVXl4IMP3uRO4fc69thjc/vtt+eiiy7Kvffem7KysvTv3z8vvfRSDj300JbzSktLM3/+/EyaNCklJSWZOHFi9t1335x00kn5+te/nmHDhiVJKioq8v3vfz9VVVUfxVMFAAAAAHaRkubm5ubWHoLtM3fu3LRp0yann356kuT6669P+/btM3z48K2uXb9+fS644IJccskl6dWrV5Jk+fLl+bd/+7e8+uqru3XurVm8eHEGDhyYI3sPT0nVga06C+wp2rYpz3/e1Le1xwAAAAD2An/va3V1dampqdnscXcG74WOOuqojB49OnfccUc2btyYmpqaXHXVVVtd99xzz+XKK6/MGWec0RKCd9ScOXNy7733bvGxbt26bfIewwAAAABA6xOD90JHHnlkpk2btt3revTokV/84hebHT/ooIO2+67gfv36pV+/fts9AwAAAADQOnyAHAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAZS39gDwfpVtylLaxrcmJElVpX8LAAAAwK6hMrDHuf6r3VNTU9PaY8AeY0NjU/Yp94scAAAAwM5RFwD2cEIwAAAAsCsoDAAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGA+wlNjQ2tfYIAAAAwF6svLUHgPcbc/fzKa16o7XHgD1KVWV5Jo/u09pjAAAAAHsxMZg9TsP6jSkpa2ztMQAAAADgY8XbRAAAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMF7qeXLl+foo4/e6nm///3vc8kll7R83b9//6xevXp3jgYAAAAA7IHKW3sAdq8VK1akvr6+5evZs2e34jQAAAAAQGsRg3exBQsW5KabbkqnTp2ybNmylJSU5Fvf+laOOeaYjB8/Pk8//XTKyspy2GGHpba2Nh06dMioUaPS1NSUN998M6tXr06XLl0yduzYVFdXp3fv3rn11ltz/PHHJ0k++9nP5sYbb8w//uM/tlyzubk5t956a1544YU0NDRk3bp1GTVqVHr27Jna2tq89dZb+eIXv5ipU6emc+fOeeqpp3LggQdm5syZue+++1JSUpJ27dpl1KhR6dKlS+rq6jJv3ry0bds2r7/+ekpLS3PzzTd/6J3Ib7zxRs4555w8/fTTadu2bZKkX79+ufrqq3PKKadscu7bb7+dVatWbbbHe6M1AAAAALBricG7wWuvvZZrrrkmPXr0yMMPP5zLL7885513XpYuXZpZs2aloqIi3/nOdzJixIhMnjw5SfKb3/wmU6dOTdu2bXPFFVdk0qRJueaaa7bpei+//HLeeOONTJs2LeXl5Zk1a1Zuv/32nH766bnxxhtTW1ubqVOnbrLm5z//eSZNmpT7778/BxxwQJ555plccsklmTdvXpLkhRdeyEMPPZSDDjooN998c+66667cfvvtHzjDoYcempqamjzyyCMZMGBAXn755fz5z39Oz549Nzv3/vvvz6RJk7bx1QQAAAAAdgUxeDc48sgj06NHjyTJ5z73uYwZMyZ33nlnJk6cmIqKiiTJWKN7qAAAIABJREFUV77ylZxwwglZt25dkmTgwIFp3759kuTcc8/Nt7/97W2OwV26dMmoUaMyc+bM/P73v8+LL76Yv/zlLx+65umnn87nPve5HHDAAUmSnj175pOf/GR+/etfJ0k6d+6cgw46KEnyT//0T3n11Ve3OsegQYMyffr0DBgwIA888EDOPffclJZu/rbUgwYNSu/evTc7Xl9fnxEjRmz1OgAAAADA9vMBcrtBefnWG3tTU1Oam5uzcePGJElZWVnLY83NzVuMqEmyYcOGzY499dRTufDCC7Nhw4accsop+cpXvrJN13+/985TWVnZcrykpGSr+yV/ewuLpUuXpr6+Po888kjOPvvsLZ7XsWPH1NTUbPbn8MMP36brAAAAAADbTwzeDV577bW88sorSZKf/vSnqa6uzle/+tVMmzYt7777bpJk8uTJOfbYY9OuXbuW8xoaGtLY2JgZM2bk1FNPTZLsv//+LXflvvTSS1m5cuVm13vmmWfSs2fPDBkyJMcdd1wee+yxTSLz3//7vU455ZTMmzcv//u//9uyx4oVK/Iv//IvO/y899lnn5x11lm59tprc/zxx6djx447vBcAAAAAsGt5m4jdYP/998/3v//9LFu2LNXV1fne976Xww47LLfddlvOOuusbNy4MZ/+9Kfzne98p2VNdXV1vvzlL2fNmjXp3r17vva1ryVJrrzyytx4442ZOXNmjj766PzzP//zZtcbPHhwrr766vz7v/97ysrK0qNHj6xZsyZ/+tOfcuSRR6Zt27bp379/Zs6c2bKme/fuueyyyzJ06NA0Nzenbdu2ueuuu7Lffvvt1HP/whe+kLvvvjt33333Tu0DAAAAAOxaJc3Nzc2tPcTHyYIFC1JbW5vHHntsm9eMGjUqnTp1yqWXXrobJ9vzLV68OAMHDsyRvYenpOrA1h4H9iht25TnP2/q29pjAAAAAHuwv/e1urq61NTUbPa4O4PZZvfcc08eeuihLT7Wt2/fDBs27COeCAAAAADYVmLwLnbCCSds113BSXLLLbfspml2raFDh2bo0KGtPQYAAAAAsAN8gBwAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGUt/YA8H6VbcpS2sa3JrxXVaV/EwAAAMDOURfY41z/1e6pqalp7TFgj7OhsSn7lPuFDgAAAGDHqAoAewkhGAAAANgZygIAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQB8ZDY0NrX2CAAAAFBY5a09ALzfmLufT2nVG609BrCLVVWWZ/LoPq09BgAAABSWGMwep2H9xpSUNbb2GAAAAADwseJtIgAAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxGAAAAAAgAIQgwEAAAAACkAMBgAAAAAoADEYAAAAAKAAxOA93IIFC/LZz352p/aYNm1aJk2atIsm2jZvvfVWzjzzzPTr1y/PPffcR3ptAAAAAGBz5a09ALvf4MGDP/JrLliwIO3atcu0adM+8msDAAAAAJsTg/cCDQ0NGT58eOrr61NZWZna2to8+eST+dWvfpXVq1fnU5/6VPr06ZM5c+Zk8uTJSZLZs2dn5syZmTJlSiZOnJiVK1dm3LhxmTVrVn70ox+lrKwsZWVlGTlyZLp165Z33303EyZMyPPPP5+mpqYccsghue6669KhQ4cPXPNBnnvuuUyYMCFr167NueeemxkzZmzy+Ntvv51Vq1Zttq6+vn6Xvm4AAAAAwP8Rg/cCq1atyhe+8IWceOKJmT9/fi6//PL069cvy5cvz9y5c1NRUZG6urpt2uvmm2/O1KlTc/jhh+eZZ57J888/n27duuWee+7Ju+++mwceeCClpaWZPHlyvvnNb+YHP/jBB675ID169MgVV1yxSZx+r/vvv/8jf9sKAAAAACg6MXgvcMQRR+TEE09Mkpx++umpra3N0qVL06VLl1RUVGzXXn379s0FF1yQXr16pUePHhk2bFiS5PHHH8/atWuzcOHCJElTU1PWrVv3oWt21KBBg9K7d+/NjtfX12fEiBE7tTcAAAAAsGVi8F6gtHTTz/lrbm7OPvvsk8rKypZjJSUlaW5ubvl6w4YNW9xr9OjRGTJkSJ577rnU1dXlzjvvzIMPPpimpqaMHDmy5cPqGhoa8qc//elD15SX79i3T8eOHdOxY8cdWgsAAAAA7JjSrZ9Ca/vd736XRYsWJUnmzp2b/fbbL506ddrknP333z+vv/561q1bl8bGxjzxxBOb7fPXv/41PXv2TElJSc4///yMGTMmr7/+ehoaGnLyySdn6tSpaWhoSJLcdtttGTVq1IeuAQAAAAD2Hu4M3gscdthhuffee/PGG2+kXbt2mThxYh555JFNzjnppJNy/PHHp2/fvunQoUO6d++eX/7yl5ucU1VVlZEjR+ayyy5LRUVFSkpKcsMNN6Rdu3a59NJLM378+Jx99tlpbm5Op06dcsstt3zoGgAAAABg71HS/N73FoBWtHjx4gwcODBH9h6ekqoDW3scYBdr26Y8/3lT39YeAwAAAD62/t7X6urqUlNTs9nj7gxmh9xzzz156KGHtvhY3759d/pD5gAAAACAXUsMZocMHTo0Q4cObe0xAAAAAIBt5APkAAAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKoLy1B4D3q2xTltI2vjXh46aq0r9rAAAAaE1+MmePc/1Xu6empqa1xwB2gw2NTdmn3C+lAAAAQGvwEzkAHxkhGAAAAFqPn8oBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAbgI7Whsam1RwAAAIBCKm/tAeD9xtz9fEqr3mjtMYDdoKqyPJNH92ntMQAAAKCQxGD2OA3rN6akrLG1xwAAAACAjxVvEwEAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAbv4RYsWJDPfvazO7XHtGnTMmnSpF000bZ58sknM378+I/0mgAAAADABytv7QHY/QYPHvyRX/Pll1/OmjVrPvLrAgAAAABbJgbvBRoaGjJ8+PDU19ensrIytbW1efLJJ/OrX/0qq1evzqc+9an06dMnc+bMyeTJk5Mks2fPzsyZMzNlypRMnDgxK1euzLhx4zJr1qz86Ec/SllZWcrKyjJy5Mh069Yt7777biZMmJDnn38+TU1NOeSQQ3LdddelQ4cOH7jmg7zwwguZPn16GhsbU11dnWuvvXaTx99+++2sWrVqs3X19fW79HUDAAAAAP6PGLwXWLVqVb7whS/kxBNPzPz583P55ZenX79+Wb58eebOnZuKiorU1dVt014333xzpk6dmsMPPzzPPPNMnn/++XTr1i333HNP3n333TzwwAMpLS3N5MmT881vfjM/+MEPPnDNBzn++ONz3nnnZeXKlZuF4CS5//77P/K3rQAAAACAohOD9wJHHHFETjzxxCTJ6aefntra2ixdujRdunRJRUXFdu3Vt2/fXHDBBenVq1d69OiRYcOGJUkef/zxrF27NgsXLkySNDU1Zd26dR+6ZkcNGjQovXv33ux4fX19RowYsVN7AwAAAABbJgbvBUpLN/2cv+bm5uyzzz6prKxsOVZSUpLm5uaWrzds2LDFvUaPHp0hQ4bkueeeS11dXe688848+OCDaWpqysiRI1s+rK6hoSF/+tOfPnRNefmOfft07NgxHTt23KG1AAAAAMCOKd36KbS23/3ud1m0aFGSZO7cudlvv/3SqVOnTc7Zf//98/rrr2fdunVpbGzME088sdk+f/3rX9OzZ8+UlJTk/PPPz5gxY/L666+noaEhJ598cqZOnZqGhoYkyW233ZZRo0Z96JoPU1ZWlo0bN+6iVwAAAAAA2FnuDN4LHHbYYbn33nvzxhtvpF27dpk4cWIeeeSRTc456aSTcvzxx6dv377p0KFDunfvnl/+8pebnFNVVZWRI0fmsssuS0VFRUpKSnLDDTekXbt2ufTSSzN+/PicffbZaW5uTqdOnXLLLbd86JoP071790ydOjXXXnttbrrppl3+mgAAAAAA26ek+b3vLQCtaPHixRk4cGCO7D08JVUHtvY4wG7Qtk15/vOmvq09BgAAAHws/b2v1dXVpaamZrPH3RnMDrnnnnvy0EMPbfGxvn377vSHzAEAAAAAu5YYzA4ZOnRohg4d2tpjAAAAAADbyAfIAQAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUQHlrDwDvV9mmLKVtfGvCx1FVpX/bAAAA0Fr8VM4e5/qvdk9NTU1rjwHsJhsam7JPuV9MAQAAgI+an8YB+EgJwQAAANA6/EQOAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMALvQhsam1h4BAAAAtqi8tQeA9xtz9/MprXqjtccA2G5VleWZPLpPa48BAAAAWyQGs8dpWL8xJWWNrT0GAAAAAHyseJsIAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxuJX1798/q1ev3mX7vfTSS/nGN76xy/YDAAAAAD4eylt7gKKbPXv2Lt1vyZIleeutt3bpngAAAADA3k8M/ogtWLAgY8eOzb777pt33nknS5cuzVNPPZVly5bl1ltvzVFHHZXf/va3WbduXWpra9OzZ880NDRk7NixWbhwYdq3b5+uXbtmyZIlmTJlyiZ7v/nmm7njjjuydu3aXHnlldlvv/1SUVGRa6+9Nkny7LPP5rbbbsvEiRNz3nnnpVevXlm8eHHWr1+fq6++OqeddlqS5MEHH8yUKVPS1NSU6urqjBw5Ml27dv3Q5zVq1Kg0NTXlzTffzOrVq9OlS5eMHTs21dXVu+eFBAAAAAC2ixjcCpYsWZJHH300hxxySDp37txyfPHixRkzZky6dOmSH//4x5kwYUJ69uyZO++8M2vWrMm8efPS1NSUiy++eIv7durUKVdccUXmzJmTCRMmZMmSJTn//PNz1VVXpU2bNpk2bVq++MUvJklWrVqVY489NuPGjcuiRYty4YUXZv78+Vm6dGmmT5+eKVOmpLq6OosXL86wYcMyf/78tG3b9kOf129+85tMnTo1bdu2zRVXXJFJkyblmmuu2ey8t99+O6tWrdrseH19/fa8jAAAAADAdhCDW0GHDh1yyCGHbPF4ly5dkiRHH310Jk+enCR54oknMnz48JSX/+2va/DgwZvdFbwlRxxxRDp37px58+ale/fueeGFFzJ+/PisXr061dXVOeecc5IkXbt2zaGHHpqFCxdm0aJFWb58eUs0TpLS0tIsW7Ysn/nMZz70egMHDkz79u2TJOeee26+/e1vbzEG33///Zk0adJW5wcAAAAAdh0xuBV80B22lZWVWzxeXl6epqamlq9LS//vc//69+/f8t833njjZmuHDBmS++67L2+99Vb69+/fcu2/h+X3KisrS1NTU84444zU1ta2HF+xYkUOOOCArTyrv63/u+bm5k3mfK9Bgwald+/emx2vr6/PiBEjtnodAAAAAGD7bbnWsUc59dRTU1dXl8bGxmzYsCEzZ85seWz27Nktf4455piUlZVl48aNLY/37t07K1asyIwZMzJ48OCW42vXrs38+fOTJC+++GKWL1+ebt265eSTT87DDz/c8iF0jz76aM4666ysX79+q3P+9Kc/TUNDQxobGzNjxoyceuqpWzyvY8eOqamp2ezP4YcfvkOvDwAAAACwde4M3gsMGzYs48aNS79+/VJVVZWDDz54kzuF3+vYY4/N7bffnosuuij33ntvysrK0r9//7z00ks59NBDW84rLS3N/PnzM2nSpJSUlGTixInZd999c9JJJ+XrX/96hg0bliSpqKjI97///VRVVW11zurq6nz5y1/OmjVr0r1793zta1/bJc8fAAAAANh5YvBH7IQTTshjjz3W8vVrr72WJDnwwAM3OX788cfn8ccfT5LMnz8/p5xySsaOHZskuf7663PwwQdvcf9OnTq1rEuS9evX5+c//3kuueSSTc4rKSnJLbfcssU9zjvvvJx33nnb/dyOO+64XHrppdu9DgAAAADY/cTgvcBRRx2V0aNH54477sjGjRtTU1OTq666aqvrnnvuuVx55ZU544wz0qtXr52aYc6cObn33nu3+Fi3bt12am8AAAAAYPcTg/cCRx55ZKZNm7bd63r06JFf/OIXmx0/6KCD8uqrr27XXv369Uu/fv22ewYAAAAAYM/gA+QAAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAAqgvLUHgPerbFOW0ja+NYG9T1Wl/3cBAACw5/JTK3uc67/aPTU1Na09BsAO2dDYlH3K/eINAAAAex4/rQLALiQEAwAAsKfyEysAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQDAVm1obGrtEQAAANhJ5a09ALzfmLufT2nVG609BgD/X1VleSaP7tPaYwAAALCTxGD2OA3rN6akrLG1xwAAAACAjxVvEwEAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAa3omnTpmXSpElbPe+73/1ufvrTn+6y644aNSrf+973dtl+SdK5c+esXLlyl+4JAAAAAOw65a09QJENHjx4m877+c9/noMOOmg3TwMAAAAAfJx9bGLw2rVrc80112TlypUpKSlJTU1NLr744gwePDi9evXK4sWLs379+lx99dU57bTTkiR33XVX5syZk7KysnTo0CHXXXddDj300EycODG/+tWvsnr16nzqU5/KXXfdlQcffDBTpkxJU1NTqqurM3LkyHTt2nWzOSZOnJjXXnsta9asyR/+8IccdNBBufnmm9OhQ4cMGTIk++67b15//fV8/vOfT1NTU1auXJlx48ZlyJAhOeaYY/LSSy9lxYoV6dq1a8aPH5+f/OQneeWVV7J8+fKUlJSkX79+m1zvr3/9a8aNG5eFCxemrKws3bp1y3XXXZeSkpKMHz8+Tz/9dMrKynLYYYeltrY2HTp02GR9fX19brzxxvzhD39IkgwYMCAXXXRRkr/d7XvmmWfmlVdeyejRo9OjR48P/Tu46667smjRovzxj3/MmWeemauuumqH/z4BAAAAgF3rYxOD58yZk8rKysyaNSsbN27M6NGj8+6772bVqlU59thjM27cuCxatCgXXnhh5s+fn6eeeioPP/xwpk+fnvbt22fGjBm55JJLMnfu3CTJ8uXLM3fu3FRUVOSFF17I9OnTM2XKlFRXV2fx4sUZNmxY5s+fn7Zt2242y4svvpi6uroccMABueGGG3LDDTfkjjvuSJK0bdu25RoTJ07cZN3rr7+e++67L+vXr8/nP//5PPvss7ngggvys5/9LOecc85mIThJJk2alLVr12bu3LkpLS3NZZddlocffjjLli3L0qVLM2vWrFRUVOQ73/lORowYkcmTJ7es3bhxYy655JJcdtllGTBgQNauXZsvfelL+eQnP5n+/fsnSU444YTcdtv/Y+/uo7yq673/v+YGuRHDKMHVqdTU9IJLTbEARbwAExRIQATv6HAjikpelkIgqOHiJi0NxRQlSNNLB6OBkVqSAl0kKpgd65A3ndOcNEltvBLIe4eZ+f3hcX4hGINig+7HY61Zi/nuvT/7vb/MUua59uz5bpP+Dj75yU+msrIyNTU16d27d0455ZR89rOf3Wq/mpqavPDCC1u9Xl1d3aTzAAAAAAA77iMTg7t37565c+dm5MiR6datW0aOHJmWLVtm9913z9ChQ5Mkhx12WPbdd9/86le/yqpVqzJkyJDsscceSZJTTjkl3/72t/PUU08lSQ499NDstttuSZKVK1dm/fr1Of300xvPV1pamqeffjoHH3zwVrOccMIJ6dixY5Jk+PDhGT58eOO2I4888l2voVevXikrK0ubNm2yzz775MUXX9zudd9///35xje+kRYtWiRJ5s6d23g95557buM1jB49Ol27ds1rr73WeOxTTz2Vv/3tbxk0aFCSpF27djn55JPzi1/8ojEG/6N53+ntWN2hQ4fstddeeeGFF7YZgxcuXNikZyUDAAAAADvPRyYGH3DAAVmxYkXWrl2btWvXZtSoURkxYkTKy7e+xLKystTX12/1en19fTZv3pwkadWq1Ravn3DCCZk6dWrja88++2w6duyYsWPHpqamJklywQUXNK7/toaGhi0+//t136lly5aNfy4pKUlDQ8NW+7wdaZNk+vTpadGiRUpKShpfe3uWd15ffX19GhoaUldXt8Vr71RfX7/FPtu68/ndvB2k/9H8yVuBvHfv3lu9Xl1dnQkTJjT5fAAAAABA05U29wA7y80335wrrrgixx57bCZOnJgePXrkySefzKZNm7J8+fIkbz2+Yf369fniF7+Ynj17prKyMi+99FKS5K677kq7du1ywAEHbLX2Mccck2XLluW5555Lktx7770ZPHhw3njjjcybNy9VVVWpqqpqfBbx8uXLs3HjxiRJRUVFevXq9b6uraysrDHQvn2uqqqqHHLIITn66KMbH41RV1eXKVOm5Oc//3l69uyZO++8M2+++WaS5JZbbsnhhx+etm3bNq6777775mMf+1iWLFmS5K3nLi9evDg9evR4X/NuT4cOHdK5c+etPvbff/8P9LwAAAAAUGQfmTuDhw8fnkmTJuXEE09Mq1atsvfee+fcc8/NsmXLsnz58lx//fUpKSnJnDlzGh+HUFNTk9NOOy319fX5xCc+kZtvvnmLu3jfdvTRR2f8+PE5++yzkyS77bZb5s6dmzZt2mxzlr322ivnnXdeXnzxxRx88MGZNm3a+7q23r1755prrskbb7yR0047bYtt559/fmbNmpVBgwalvr4+Rx11VE4//fTU1dXlu9/9bgYPHpy6urrst99++d73vrfFsS1atMgNN9yQGTNmZP78+amtrc2AAQMybNiw9zUvAAAAALDrKWl4t5/l/whYv359jj/++Dz++OP/tHPOmTMnzz//fGbMmPFPO+dHxWOPPZYhQ4bkwN7fSEmbvZt7HAD+W+uW5blrZv/mHgMAAIDteLuvVVZWpnPnzltt/8jcGcwH68ILL8wf//jHbW775je/maOOOuqfPBEAAAAAsCM+0jH405/+9D/1ruAk+drXvvZPPd8/y+zZs5t7BAAJf8k4AAAgAElEQVQAAADgffjI/AI5AAAAAADenRgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUQHlzDwDv1KplWUpb+tIE2FW0aeW/yQAAAB8FvrtjlzNtbPd07ty5uccA4O/Ubq5Pi3I/UAQAAPBh5rs6AGC7hGAAAIAPP9/ZAQAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAOwUtZvrm3sEAAAA/oHy5h4A3unyeQ+ltM1TzT0GADugTavy3HJZ3+YeAwAAgH9ADGaX8/obdSkp29zcYwAAAADAR4rHRAAAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABvO8YfOmll+bf/u3ftrtf796988gjjyRJDjrooDz//PPv99RN8uSTT+a4447L4MGD85//+Z8f6LnmzJmTKVOm7PBxf//efJD+8pe/ZMiQIR/Y+iNGjEhVVdUHtj4AAAAA8N697xj8wAMPpL6+fmfM8oFYvnx5vvCFL2Tx4sU58MADm3ucZtWxY8dUVlY29xgAAAAAQDMob+qOmzZtyje/+c08//zzKSkpSefOnbPHHnukpqYml1xySa644or8y7/8S771rW/llVdeSU1NTT796U9n9uzZad++/TbX3LhxY84666wce+yx+drXvrbV9h/84AepqqrKbrvtlpYtW2batGk58MADM2LEiAwdOjQnnXRSkmTkyJH5yle+kiFDhuSggw7KwIED87vf/S7jxo3LnXfemc2bN+f888/P9ddfn6uuuiqPPPJIXn/99bz22muZNGlSjjvuuNTV1eXqq6/OihUrUl5env333z8zZ85M27Zts3jx4tx2222pr6/P7rvvnokTJ+awww7b5jX96U9/yogRI7Jx48bss88+mTZtWj7xiU/k5ZdfzqxZs/LEE0+ktrY2hx56aCZPnpy2bdtucfzKlStz/fXXp7a2Nrvttlv+9//+3+nZs2cGDx6cr33ta+ndu3ceeOCBjB49OitXrsy//Mu/5Ac/+EH+/Oc/5/LLL99ircrKytx1112pra1Nknzve99Lv3798vjjj6eysjL33HNPWrdunT/+8Y8pLS3NrFmz0qlTp2zYsCFTp07NH/7wh+yxxx458MAD09DQkG9/+9tN+lqpr6/PZZddlpqamlx77bVp3bp1k44DAAAAAD44Tb4z+O67706rVq2yZMmSLFq0KA0NDRk6dGg6dOiQmTNnplu3bvnxj3+cfv36paKiIsuXL09ZWVmWLFmyzfX++te/5l//9V8bI+c7vfzyy5k9e3buvPPO/OQnP8mwYcPy8MMPN2nWrl27ZtmyZRk0aFBOPfXUHHfccfn+97+fdevW5amnnsqdd96ZpUuXZvz48bn22muTJBUVFfn1r3+dxYsX52c/+1n23HPP3HHHHXnkkUdSUVGR2267LUuWLMkll1yS8847L6+99to2z/3HP/4xV199dZYuXZpPfepTmT59epJk1qxZ+dznPpfKysrcfffdadu2ba6++uotjv2v//qvTJ48Od/5zneydOnSzJgxIxMmTMjTTz+d448/PqtWrUqS/N//+3+z11575YEHHkjy1t3P/fr12+Y8f/jDHzJ//vz85Cc/SWnpln/djzzySCZOnJilS5emW7duuemmm5IkM2bMSMeOHbNs2bIsWLAgv/vd75r0vidJXV1dJkyYkDfeeCM33HDDNkNwTU1NHnvssa0+qqurm3weAAAAAGDHNPnO4O7du2fu3LkZOXJkunXrlpEjR2b//fffYp8LL7wwDz30UObPn5+nn3461dXV6dKlyzbXGzduXNq2bZthw4Ztc3vbtm1z9NFHZ/DgwTn22GPTvXv3DBgwoEmzHnnkkdt8/dBDD82kSZOyaNGiPPPMM3n00UfzyiuvJEnuv//+DBw4MG3atEmSXHHFFUmSq666KuvXr8/pp5/euE5paWmefvrpHHzwwVud4/jjj0+HDh2SJKecckrOOOOMJG/d8fvoo4/m7rvvTpLU1tbmE5/4xBbHrlmzJl/60pca39eDDz44RxxxRB544IEcf/zxGTt2bOOs5557blavXp3/9b/+V9avX58vfvGL27zmAw44IHvuuec2tx100EH59Kc/nST5H//jf+Txxx9PkvziF79IRUVFSkpK8rGPfSxDhgzJ73//+22u8U7XXHNNNm7cmHvuuSfl5dv+8lq4cGGuv/76Jq0HAAAAAOwcTY7BBxxwQFasWJG1a9dm7dq1GTVqVCZNmrTFPhdffHFeffXV9O/fP0cddVRee+21NDQ0bHO9Sy65JBUVFbnuuuty0UUXZd26dZk6dWrj9qqqqtx000154oknsmbNmsybNy8VFRWZN29ekmyx7tuPQXjbuz2WYNWqVZk2bVpGjRqVnj175vDDD8/MmTOTJC1atEhJSUnjvhs3bswrr7yS+vr6nHDCCVvM9uyzz6Zjx44ZO3ZsampqkiQXXHBBkmxx9219fX1atGjR+Ofvfve76dSpU5K37nx+59zbeq8aGhpSV1eX/fffP23atMmKFSvSpk2bDBgwIHPmzMl9992XPn36pLS0NFOmTGm8i/fUU09Ny5Yt06pVq22+F0m22Pb3115eXr7Fc6D/ftv29O3bNx//+MczceLE3H777SkrK9tqn+HDh6d3795bvV5dXZ0JEyY0+VwAAAAAQNM1+TERN998c6644ooce+yxmThxYnr06JEnn3wyZWVlqaurS5L88pe/zLhx4zJw4MDsscceeeihhxq3vdPhhx+eWbNmZeHChfnVr36VQw45JFVVVY0fzzzzTHr37p2OHTtm1KhRufDCCxvvXG3fvn2eeOKJJMkzzzzT+OftWb16dXr06JERI0akS5cuue+++xrnO/roo/PTn/40r7/+epK37nC97bbbcswxx2TZsmV57rnnkiT33ntvBg8enDfeeCPz5s1rnLdPnz5J3roD+MUXX0xDQ0PuuOOO9OzZM0lyzDHH5NZbb01dXV3q6uoyadKkfO9739tivm7dumXt2rWNj0t48skn8/DDD+eoo45K8tZdx9/97nfTo0ePtGvXLvvss0/mzZvX+IiIGTNmNM5z2mmnNek92ZZevXrlxz/+cZLk1VdfzdKlS5schA899NCMGzcub775ZuNjJ96pQ4cO6dy581Yf77zTHAAAAADYeZp8Z/Dw4cMzadKknHjiiWnVqlX23nvvTJw4MXV1dZk4cWIuvfTSXHzxxbnooovSrl27tGjRIl27ds3TTz/9rmt+6lOfyqRJkzJx4sTcfffd2WOPPRq3feYzn8mZZ56ZM844I61bt05paWnjXbznnHNOJk+enAEDBmSfffZJt27dmnQNp512Wi6++OIMGDAgZWVlOeqoo7Jx48a89NJLGTZsWJ5//vkMHTo0yVuPaJg8eXJat26d8ePH5+yzz06S7Lbbbpk7d27j4yTe6aCDDsr48eOzadOmfP7zn8+3vvWtJMnUqVMza9asfOUrX0ldXV0OOeSQfPOb39zi2Ld/ad3FF1+czZs3p7S0NN/+9rcbI+nxxx+f73//++nRo0eSpEePHrnjjjvypS99qUnX31STJ0/O5ZdfngEDBqRt27bZa6+9/uEdxu9UXl6eq666Kqecckp69OiRQw89dKfOBwAAAADsuJKGd3uOA4X1f/7P/8nnP//5fPGLX0x9fX3OO++89OrVK8OHD/9Az/vYY49lyJAhObD3N1LSZu8P9FwA7FytW5bnrpn9m3sMAACAQnu7r1VWVqZz585bbW/yncEUx+c///nMmjUr9fX1qa2tTffu3XPyySdn5syZWbt27TaPGTVqVAYNGvRPnhQAAAAAaCoxmK188YtfTGVl5VavX3LJJc0wDQAAAACwMzT5F8gBAAAAAPDhJQYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFUN7cA8A7tWpZltKWvjQBPkzatPLfbQAAgF2d79zY5Uwb2z2dO3du7jEA2EG1m+vTotwPHQEAAOyqfMcGAOwUQjAAAMCuzXdtAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAB8KtZvrm3sEAACAD7Xy5h4A3unyeQ+ltM1TzT0GALuQNq3Kc8tlfZt7DAAAgA81MZhdzutv1KWkbHNzjwEAAAAAHykeEwEAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAYjBAAAAAAAFIAbvYtatW5dx48YlSebMmZMpU6YkSUaMGJGqqqqddp6DDjoozz///E5br7KyMiNHjtxp6wEAAAAAO5cYvIs55JBDMnfu3OYeAwAAAAD4iClv7gGKbMGCBamoqMjuu++ebt265dZbb80Pf/jDTJ06Nffdd1+T11m3bl2mT5+el19+OaWlpTn//PPTr1+/VFdXZ/r06fl//+//JUkGDRqUMWPGbHX8okWLcuutt6akpCRt27bNpEmTcuihh6aysjJ33XVXamtrkyR33XVXysrK3nWOF198MWeffXaef/75tG/fPtOmTcs+++yzg+8KAAAAAPBBEIObyYMPPpjbb789ixYtSvv27TN79uzU1dXt8DqbN2/Oeeedl6lTp6Zv37556qmnMnr06Bx99NEZN25czj///AwaNCibNm3KmWeemU9+8pM56aSTGo9fs2ZNrr/++ixcuDAdO3bM6tWrM27cuNxzzz1Jkj/84Q9Zvnx59txzz+3O8qc//SnXXHNNDjjggNx6662ZMGFC7rrrrq32q6mpyQsvvLDV69XV1Tt8/QAAAABA04jBzWTlypXp27dv2rdvnyQZOXJkbrzxxh1e5/e//33efPPN9O3bN0my7777ZuXKlamurs7f/va3DBo0KEnSrl27nHzyyfnFL36xRQy+//77069fv3Ts2DFJ0qNHj3zyk5/Mb37zmyTJAQcc0KQQnCRdu3bNAQcckCQZOnRoZs6cmZdeeil77LHHFvstXLgw119//Q5fKwAAAADw3onBzaS8vDwNDQ2Nn5eWNu3xzStWrMh1112XJOnQoUMuvvjilJSUbLFPdXV16uvrtzq2vr5+q7uPt7VfQ0ND436tWrVq0lzJltfQ0NCQkpKSlJdv/SU2fPjw9O7de6vXq6urM2HChCafDwAAAABoOr9Arpn06tUr9957bzZs2JAkqaioaNJxffr0SVVVVaqqqjJv3rzst99+adOmTVauXJkkeeaZZ3LqqafmE5/4RD72sY9lyZIlSZJNmzZl8eLF6dGjxxbr9ezZM/fcc0/+8pe/JElWr16dZ599NkccccQOX9OvfvWr/OlPf0qS3HHHHfnSl76U1q1bb7Vfhw4d0rlz560+9t9//x0+JwAAAADQNO4MbiZdu3bN6NGjc+aZZ6Zly5bp1KnTNsPp9uy22275/ve/nxkzZmT27NkpLS3Nd77znbRv3z433HBDZsyYkfnz56e2tjYDBgzIsGHDtji+e/fuOf/883PWWWeloaEhrVu3zk033dTkR0P8vYMPPjjTpk1LTU1NOnbsmCuvvHKH1wAAAAAAPhglDX//rAL+adatW5eHH344Y8aMSZLcc889ufXWW5t8h/BH0WOPPZYhQ4bkwN7fSEmbvZt7HAB2Ia1blueumf2bewwAAIBd2tt9rbKyMp07d95quzuDm8l+++2X+fPnp3///ikrK0u7du0yY8aM5h7rXf3Xf/1Xvv71r29zW+vWrQsdsQEAAADgw0AMbiZt27bN7Nmzm3uMJvvc5z6Xqqqq5h4DAAAAAHiP/AI5AAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACKG/uAeCdWrUsS2lLX5oA/P/atPL/BQAAgPfLd1bscqaN7Z7OnTs39xgA7GJqN9enRbkfagIAAHivfEcFAHwoCMEAAADvj++qAAAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAPhvtZvrm3sEAACAD0x5cw8A73T5vIdS2uap5h4DgIJp06o8t1zWt7nHAAAA+MCIwexyXn+jLiVlm5t7DAAAAAD4SPGYCAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACiA9xyDL7300vzbv/3bdvfr3bt3HnnkkSTJQQcdlOeff/69nnKHPPnkkznuuOMyePDg/Od//uc/5ZzbMmnSpNxwww3Ndv6/N2XKlKxateoDWbuysjIjR478QNYGAAAAAN6/9xyDH3jggdTX1+/MWXaq5cuX5wtf+EIWL16cAw88sLnH2SXMmDEjxx57bHOPAQAAAAA0g+3G4E2bNmXcuHEZNGhQBg8enKlTp+bKK69MTU1NLrnkkqxZsybPPPNMxowZk1NPPTW9e/fOV7/61bz44ovvuubGjRszdOjQzJkzZ5vbf/CDH2TgwIE5+eSTc/rppzfe2TtixIhUVVU17jdy5MhUVlYmeeuu44svvjj9+vXLkiVLcuedd+b+++/P+eefn4aGhlx55ZU55ZRTMnDgwBx33HFZvnx5kqSuri5XXXVV+vbtm/79++eCCy7Iyy+/nCRZvHhxhgwZkkGDBuWMM87Ib3/7223O+/rrr2fy5MkZNmxYTjjhhPTv3z+PPvpo4/Z///d/z/Dhw9O/f/9Mnjw5r7zySpKkuro6o0aNysCBAzNw4MDMnz8/SfLjH/94i7ts//rXv6ZLly7ZtGlTampqcsEFF2TIkCEZOHBgrrzyymzevHmbc/39e/Lggw9u8f4ddNBBufHGGxv/zm6++ebG4xYsWJDjjz8+gwcPzpVXXplOnTq9y9/ktt1///3p1atX/v3f/32HjgMAAAAAPjjbjcF33313WrVqlSVLlmTRokVpaGjI0KFD06FDh8ycOTPdunXLj3/84/Tr1y8VFRVZvnx5ysrKsmTJkm2u99e//jX/+q//msGDB+drX/vaVttffvnlzJ49O3feeWd+8pOfZNiwYXn44YebdDFdu3bNsmXLMmjQoJx66qk57rjj8v3vfz/r1q3LU089lTvvvDNLly7N+PHjc+211yZJKioq8utf/zqLFy/Oz372s+y5556544478sgjj6SioiK33XZblixZkksuuSTnnXdeXnvtta3Ou3r16pSXl2fhwoW55557cuKJJ+amm25q3P7ss89m/vz5ufvuu/PKK6/kxhtvTF1dXcaNG5eTTjopS5cuze23354lS5akqqoq/fv3z2OPPZY///nPSZIlS5bkuOOOS7t27TJx4sSceOKJqaysTGVlZf785z/nlltu2e57ctRRR221bbfddktFRUVuuOGGXHvttXnppZfy4IMP5vbbb09FRUUWL16cli1bpq6urknvf/LWHdnTp0/P/Pnzc+ihh25zn5qamjz22GNbfVRXVzf5PAAAAADAjinf3g7du3fP3LlzM3LkyHTr1i0jR47M/vvvv8U+F154YR566KHMnz8/Tz/9dKqrq9OlS5dtrjdu3Li0bds2w4YN2+b2tm3b5uijj87gwYNz7LHHpnv37hkwYECTLubII4/c5uuHHnpoJk2alEWLFuWZZ57Jo48+2nh37v3335+BAwemTZs2SZIrrrgiSXLVVVdl/fr1Of300xvXKS0tzdNPP52DDz54i/WPO+64fPrTn84dd9yRP/3pT1m7dm322GOPxu0nnXRS2rZtmyQZMmRIrr322gwePDh/+9vfMmjQoCRJu3btcvLJJ+cXv/hFTjrppPTv3z+LFy/O+PHjU1lZmSuuuCKvvvpq1qxZkw0bNuTGG29M8tZdyS1bttzh9yRJ+vTpkyQ5+OCDU19fn7/97W9ZuXJl+vbtm/bt2yd56+7rt8+1Pb///e9z4YUX5txzz83nPve5d91v4cKFuf7665u0JgAAAACwc2w3Bh9wwAFZsWJF1q5dm7Vr12bUqFGZNGnSFvtcfPHFefXVV9O/f/8cddRRee2119LQ0LDN9S655JJUVFTkuuuuy0UXXZR169Zl6tSpjdurqqpy00035YknnsiaNWsyb968VFRUZN68eUmyxbq1tbVbrN26dettnnPVqlWZNm1aRo0alZ49e+bwww/PzJkzkyQtWrRISUlJ474bN27MK6+8kvr6+pxwwglbzPbss8+mY8eOGTt2bGpqapIkF1xwQV544YXcfvvtGTFiRI4//vh89rOfzbJlyxqPKysra/xzfX19WrRosc3nLdfX1zfehXvqqafm/PPPT48ePVJSUpIuXbrk5ZdfTkNDQ370ox+lXbt2Sd56jEdpaWlWrFiR6667LknSoUOHxvfr3d6TJGnVqlXjn0tKStLQ0JDy8vIt3uPS0qY/VrpNmzaZM2dOzj333PTp02eraP624cOHp3fv3lu9Xl1dnQkTJjT5fAAAAABA02239N1888254oorcuyxx2bixInp0aNHnnzyyZSVlTWGy1/+8pcZN25cBg4cmD322CMPPfTQuz5a4PDDD8+sWbOycOHC/OpXv8ohhxySqqqqxo9nnnkmvXv3TseOHTNq1KhceOGFefzxx5Mk7du3zxNPPJEkeeaZZxr/vD2rV69Ojx49MmLEiHTp0iX33Xdf43xHH310fvrTn+b1119PklxzzTW57bbbcswxx2TZsmV57rnnkiT33ntvBg8enDfeeCPz5s1rnLdPnz755S9/ma985SsZPnx4OnXqlOXLl29x/T/96U/z2muvpba2NnfddVd69uyZfffdNx/72McaH6exadOmLF68OD169Ejy1t26n/zkJzNr1qwMHz48yVt3TR9xxBFZsGBBkrfuCh47dmzuvPPO9OnTp3Gmt0Pwe9GrV6/ce++92bBhQ5K3HqPRVJ/5zGdy5JFHZvz48ZkwYULeeOONbe7XoUOHdO7ceauPd95xDgAAAADsPNu9M3j48OGZNGlSTjzxxLRq1Sp77713Jk6cmLq6ukycODGXXnppLr744lx00UVp165dWrRoka5du+bpp59+1zU/9alPZdKkSZk4cWLuvvvuLR6p8JnPfCZnnnlmzjjjjLRu3TqlpaWNd/Gec845mTx5cgYMGJB99tkn3bp1a9JFnnbaabn44oszYMCAlJWV5aijjsrGjRvz0ksvZdiwYXn++eczdOjQJG9F2MmTJ6d169YZP358zj777CRvPV937ty5jY+T+HtnnXVWLr300vzsZz9LSUlJjjzyyPz85z9vvPt3v/32y1e/+tW8/PLL6dq1a84+++y0aNEiN9xwQ2bMmJH58+entrY2AwYM2OLxGcOHD8+0adO2iLtXX311pk+fnoEDB+bNN99Mz549M3r06Ca9D03RtWvXjB49OmeeeWZatmyZTp06/cO7i7flq1/9alasWJHvfOc7W9xZDQAAAAA0n5KGd3ueA4W0bt26PPzwwxkzZkyS5J577smtt966Q3cIv1ePPfZYhgwZkgN7fyMlbfb+wM8HAH+vdcvy3DWzf3OPAQAA8J693dcqKyvTuXPnrbZv985gimW//fbL/Pnz079//5SVlaVdu3aZMWNG1qxZk1mzZm3zmH322afxecUAAAAAwK5JDGYLbdu2zezZs7d6ff/9909VVVUzTAQAAAAA7Azb/QVyAAAAAAB8+InBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAACjdUc0AACAASURBVABAAYjBAAAAAAAFIAYDAAAAABSAGAwAAAAAUABiMAAAAABAAZQ39wDwTq1alqW0pS9NAP652rTy/x4AAOCjzXc97HKmje2ezp07N/cYABRQ7eb6tCj3g1MAAMBHk+92AAD+mxAMAAB8lPmOBwAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAA+kmo31zf3CAAAsEspb+4B4J0un/dQSts81dxjAAAfYm1aleeWy/o29xgAALBLEYPZ5bz+Rl1KyjY39xgAAAAA8JHiMREAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGIwAAAAAEABiMEAAAAAAAUgBgMAAAAAFIAYDAAAAABQAGLwLm7dunUZN25ckmTOnDmZMmVKkmTEiBGpqqraobWmTJmSVatW7fQZ169fn06dOu30dQEAAACAnae8uQfgHzvkkEMyd+7cnbLWjBkzdso6AAAAAMCHjxi8C1mwYEEqKiqy++67p1u3brn11lvzwx/+MFOnTs19993XpDUaGhpy1VVXZfXq1WnRokU+/vGPZ+bMmenYsWNGjBiRoUOHZt99981ll13WeMwLL7yQFi1aZNWqVampqcn06dOzfv361NbWpkePHrnoootSXr79L5XLL788v/3tb1NSUpKvf/3r6dmz53t+LwAAAACAnUsM3kU8+OCDuf3227No0aK0b98+s2fPTl1d3Q6v8x//8R9ZunRpVq1albKysixYsCC/+c1v0rdv38Z9DjvssMZHTFRXV2f06NG55pprkiQTJ07Mqaeemn79+qW2tjYXXXRRbrnllpx11ln/8Lx1dXX5whe+kGnTpuV3v/tdRo0alZ///Odp3779VvvW1NTkhRde2Or16urqHb5eAAAAAKBpxOBdxMqVK9O3b9/GeDpy5MjceOONO7zOvvvumw4dOmTQoEE55phjcswxx6R79+7b3Pe5557L2LFjM3Xq1HTp0iWvvvpq1qxZkw0bNjSe+/XXX0/Lli23e942bdpk8ODBSZL/+T//Zz772c/m0UcfTZ8+fbbad+HChbn++ut3+NoAAAAAgPdODN5FlJeXp6GhofHz0tKm/W6/FStW5LrrrkuSdOjQIfPmzcuiRYvy29/+NmvXrs3MmTPTpUuXfOtb39riuA0bNmTMmDEZM2ZMvvzlLydJ6uvr09DQkB/96Edp165dkmTTpk1NmqWsrGyLzxsaGtKiRYtt7jt8+PD07t17q9erq6szYcKE7Z4LAAAAANhxTSuOfOB69eqVe++9Nxs2bEiSVFRUNOm4Pn36pKqqKlVVVZk3b14eeeSRnHzyyTn44IMzbty4jBw5Mo8//vgWx7z66qs555xz8uUvfzlnnHFG4+tt27bNEUcckQULFiR5667gsWPH5s4779zuHC+99FKWLVuWJPn1r3+d5557Locddtg29+3QoUM6d+681cf+++/fpGsGAAAAAHacO4N3EV27ds3o0aNz5plnpmXLlunUqVNat269w+t06dIl3bt3z6BBg7L77rtnt912y9SpU7fY57bbbsvvfve7NDQ0ZNWqVY13JN988825+uqrM3369AwcODBvvvlmevbsmdGjR2/3vHvuuWdWr16dG2+8MWVlZbnuuusa7y4GAAAAAJpfScPfP5uAZrNu3bo8/PDDGTNmTJLknnvuya233trkO4Q/Ch577LEMGTIkB/b+Rkra7N3c4wAAH2KtW5bnrpn9m3sMAAD4p3q7r1VWVqZz585bbXdn8C5iv/32y/z589O/f/+UlZWlXbt2mTFjRnOP1ej000/PK6+8ss1tV199dQ444IB/8kQAAAAAwI4Qg3cRbdu2zezZs5t7jHd1xx13NPcIAAAAAMD74BfIAQAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUgBgMAAAAAFAAYjAAAAAAQAGIwQAAAAAABSAGAwAAAAAUQHlzDwDv1KplWUpb+tIEAN67Nq38WwIAAN7Jv5LZ5Uwb2z2dO3du7jEAgA+52s31aVHuB+EAAOBt/nUMAMBHkhAMAABb8i9kAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAAAAAApADAYAAAAAKAAxGAAAAACgAMRgAAAAAIACEIMBAGA7ajfXN/cIAADwvpU39wDwTpfPeyilbZ5q7jEAAJIkbVqV55bL+jb3GAAA8L6JwexyXn+jLiVlm5t7DAAAAAD4SPGYCAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMRgAAAAAoADEYAAAAACAAhCDAQAAAAAKQAwGAAAAACgAMXgXt27duowbNy5JMmfOnEyZMiVJMmLEiFRVVe3QWlOmTMmqVat2+owAAAAAwK6vvLkH4B875JBDMnfu3J2y1owZM3bKOgAAAADAh48YvAtZsGBBKioqsvvuu6dbt2659dZb88Mf/jBTp07Nfffd16Q1GhoactVVV2X16tVp0aJFPv7xj2fmzJnp2LFjRowYkaFDh2bffffNZZdd1njMCy+8kBYtWmTVqlWpqanJ9OnTs379+tTW1qZHjx656KKLUl7+7l8q69evz+mnn55+/frl0UcfzYYNGzJ+/PgMGjTofb8nAAAAAMDOIQbvIh588MHcfvvtWbRoUdq3b5/Zs2enrq5uh9f5j//4jyxdujSrVq1KWVlZFixYkN/85jfp27dv4z6HHXZY4yMmqqurM3r06FxzzTVJkokTJ+bUU09Nv379Ultbm4suuii33HJLzjrrrH943r/85S854ogjcskll2TFihWZOnXqu8bgmpqavPDCC1u9Xl1dvcPXCwAAAAA0jRi8i1i5cmX69u2b9u3bJ0lGjhyZG2+8cYfX2XfffdOhQ4cMGjQoxxxzTI455ph07959m/s+99xzGTt2bKZOnZouXbrk1VdfzZo1a7Jhw4bGc7/++utp2bLlds9bUlKS3r17J0k6deqUF1988V33XbhwYa6//vodvjYAAAAA4L0Tg3cR5eXlaWhoaPy8tLRpv9tvxYoVue6665IkHTp0yLx587Jo0aL89re/zdq1azNz5sx06dIl3/rWt7Y4bsOGDRkzZkzGjBmTL3/5y0mS+vr6NDQ05Ec/+lHatWuXJNm0aVOTZiktLc1uu+2W5K0w/I8MHz68MRz/verq6kyYMGG75wIAAAAAdpwYvIvo1atXJk+enHPOOScf//jHU1FR0aTj+vTpkz59+jR+/sgjj2TGjBm54447cvjhh2evvfbKwoULtzjm1VdfzTnnnJMvf/nLOeOMMxpfb9u2bY444ogsWLAgX//61/P6669n7NixOe6443L22WfvnAvNW9G6Q4cOO209AAAAAGD7xOBdRNeuXTN69OiceeaZadmyZTp16pTWrVvv8DpdunRJ9/+vvfuP1bKu/zj+uuEoPxf+YBwlXTZKyQOTnFAo2dIWDnbAEaU2z/zR0TFZ5DI2wuKbC8Tl3HQ7/aFfmxBNJM08EnMtKxlE/lhaoeQo0hRBIDVMwwMHru8fxfmKBxW9D+c+3tfj8d+57us+9+fD3jve19P7XGfixJx//vkZMmRIjjzyyHz7298+4Jxly5blySefTFEUWb16ddcnkm+77bbcdNNNWbhwYZqbm7N79+6cffbZufzyy3tkjwAAAABA7VSKN9+bgJpZv359Hn300Xz1q19NkjzwwANZunTpIX9CuB489dRTmTFjRj5+zjdSGXxcrZcDAJAkGTSgIT+5fmqtlwEAAO9qf1+7995709TU1O1xnwzuIz760Y/mhz/8YaZOnZr+/ftn2LBhWbRoUa2X1eUrX/lKXn/99YM+dtNNN+VjH/tYL68IAAAAAHgvxOA+YujQobn55ptrvYy3deedd9Z6CQAAAABAFfrVegEAAAAAABx+YjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACDbVeALzVwAH902+A0QQA+obBA70vAQCgPnhnS59z3RUT09TUVOtlAAB02dO5L0c0+KU6AAA+2LyjBQCAdyEEAwBQD7yrBQAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAoATEYAAD6kD2d+2q9BAAA6lRDrRcAb/U///u79Bv8bK2XAQDQ6wYPbMiSBZNrvQwAAOqUGEyf80bH3lT6d9Z6GQAAAABQV9wmAgAAAACgBMRgAAAAAIASEIMBAAAAAEpADAYAAAAAKAExGAAAAACgBMRgAAAAAIASEIMBAAAAAEpADAYAAAAAKAExGAAAAACgBMRgAAAAAIASEIMBAAAAAEpADAYAAAAAKAExuOTa29vT0tLSo99z69ataW5uzrRp07Ju3boe/d4AAAAAwPvTUOsFUH8eeeSRDB06NMuXL6/1UgAAAACA/xKD69g999yTpUuXplKpZOjQoZk3b15OPfXU3HDDDXnooYdyzDHH5MQTT+w6/7XXXsvChQuzfv36VCqVjBs3LvPmzcvQoUPT0tKSYcOG5ZlnnsmUKVMye/bsg77munXrcvPNN2fnzp350pe+lLvvvrvbOdu3b8+OHTu6Hd+0aVPPbR4AAAAAOIAYXKcefvjhtLW1ZcWKFWlsbMzatWsza9astLS0ZMOGDVm1alUqlUpmzZrV9ZxFixaloaEhK1euTFEUmT9/fhYvXpxFixYlSQYNGpRVq1a94+ueeeaZmTNnTu6///4sWbLkoOesWLEibW1tPbZXAAAAAODdicF1as2aNTnvvPPS2NiYJJk0aVKGDx+etra2zJ8/PwMGDEiSzJw5s+t2DqtXr84dd9yRfv3+cyvpSy65JK2trV3f84wzzuiRtV1wwQU555xzuh3ftGlT5s6d2yOvAQAAAAAcSAyuU/v27et2rCiKdHZ2piiKrmMNDQ1v+5x9+/Zl7969XV8PHDiwR9Y2YsSIjBgxoke+FwAAAABwaPrVegEcHmeffXYeeOCBbNu2LUmydu3abNmyJddcc03uv//+7Nq1K52dnWlvbz/gOcuWLeuKwMuWLcukSZNqtQUAAAAAoAf5ZHCdmjhxYmbPnp3W1tYURZFBgwbl1ltvzemnn56dO3emubk5Rx11VEaPHp1XX301SXLttddm8eLFmTZtWvbs2ZNx48ZlwYIFNd4JAAAAANATKsWb7xkANfTUU09lxowZ+fg530hl8HG1Xg4AQK8bNKAhP7l+aq2XAQDAB9T+vnbvvfemqamp2+M+Gcx7dvvtt2flypUHfWzq1Km58sore3lFAAAAAMC7EYN5z1pbW9Pa2lrrZQAAAAAA74E/IAcAAAAAUAJiMAAAAABACYjBAAAAAAAlIAYDAAAAAJSAGAwAAAAAUAJiMAAAAABACYjBAAAAAAAlIAYDAAAAAJRAQ60XAG81cED/9BtgNAGA8hk80HsgAAAOH+826XOuu2Jimpqaar0MAICa2NO5L0c0+AU+AAB6nneZAADQhwjBAAAcLt5pAgAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAAFACYjAAAAAAQAmIwQAAAAAAJSAGAwAAAACUgBgMAAAAANS1PZ37ar2EPqGh1guAt/qf//1d+g1+ttbLAAAAAKAODB7YkCULJtd6GX2CGEyf80bH3lT6d9Z6GQAAAABQV9wmAgAAAACgBMRgAAAAAIASEIMBAAAAAEpADAYAAAAAKAExGAAAAACgBMRgAAAAAIASEIMBAAAAAEpADAYAAAAAKAExGAAAAACgBMRgAAAAAIASEIMBAAAAAEpADAYAAAAAKAExuITa29vT0tJS62UAAAAAAL2oodYLoHy2b9+eHTt2dDu+adOmGqwGAAAAAMpBDK4z99xzT5YuXZpKpZKhQ4dm3rx5OfXUU3PDDTfkoYceyjHHHJMTTzyx6/zXXnstCxcuzPr161OpVDJu3LjMmzcvQ4cOTUtLS4YNG5ZnnnkmU6ZMyezZs9/2dVtaWjJ27Nj86U9/ypYtW3LaaaflxhtvTEND9xFbsWJF2traDsv+AQAAAICDE4PryMMPP5y2trasWLEijY2NWbt2bWbNmpWWlpZs2LAhq1atSqVSyaxZs7qes2jRojQ0NGTlypUpiiLz58/P4sWLs2jRoiTJoEGDsmrVqkN6/WeeeSZLly5NR0dHpkyZkt/+9rf57Gc/2+28Cy64IOecc06345s2bcrcuXPf5+4BAAAAgHciBteRNWvW5LzzzktjY2OSZNKkSRk+fHja2toyf/78DBgwIEkyc+bMLF++PEmyevXq3HHHHenX7z+3j77kkkvS2tra9T3POOOMQ379z33uc+nfv38GDx6cj3zkI3n55ZcPet6IESMyYsSI97VHAAAAAOD98Qfk6si+ffu6HSuKIp2dnSmKouvYm2/d8Nbn7Nu3L3v37u36euDAgYf8+vtjc5JUKpUDXhMAAAAAqC0xuI6cffbZeeCBB7Jt27Ykydq1a7Nly5Zcc801uf/++7Nr1650dnamvb39gOcsW7asKwIvW7YskyZNqtUWAAAAAIDDxG0i6sjEiRMze/bstLa2piiKDBo0KLfeemtOP/307Ny5M83NzTnqqKMyevTovPrqq0mSa6+9NosXL860adOyZ8+ejBs3LgsWLKjxTgAAAACAnlYp/C4/fcRTTz2VGTNm5OPnfCOVwcfVejkAAAAA1IFBAxryk+un1noZvWJ/X7v33nvT1NTU7XGfDOaQ3H777Vm5cuVBH5s6dWquvPLKXl4RAAAAAPBeiMEcktbW1rS2ttZ6GQAAAADA++QPyAEAAAAAlIAYDAAAAABQAmIwAAAAAEAJiMEAAAAAACUgBgMAAAAAlIAYDAAAAABQAmIwAAAAAEAJiMEAAAAAACXQUOsFwFsNHNA//QYYTQAAAACqN3igzrSffwn6nOuumJimpqZaLwMAAACAOrGnc1+OaHCTBP8CAAAAAEBdE4L/w78CAAAAAEAJiMEAAAAAACUgBgMAAAAAlIAYDAAAAABQAmIwAAAAAEAJiMEAAAAAACUgBgMAAAAAlIAYDAAAAABQAmIwAAAAAEAJiMEAAAAAACUgBgMAAAAAlIAYDAAAAABQAmIwAAAAAEAJiMEAAAAAACXQUOsFwH4dHR1Jkk2bNtV4JQAAAADwwbO/q+3vbG8lBtNnbNiwIUkyd+7cGq8EAAAAAD64Nm/enNNPP73bcTGYPmPUqFFJkuuvvz6jR4+u8WqgZ2zatClz587NjTfe2DXj8EFnrqlH5pp6ZbapR+aaemSu6SkdHR3ZvHlzJk2adNDHxWD6jA996ENJktGjR6epqanGq4GeNWrUKHNN3THX1CNzTb0y29Qjc009Mtf0hIN9Ing/f0AOAAAAAKAExGAAAAAAgBIQgwEAAAAASkAMBgAAAAAogf7f/e53v1vrRcB+Q4YMyYQJEzJkyJBaLwV6jLmmHplr6pG5pl6ZbeqRuaYemWt6Q6UoiqLWiwAAAAAA4PBymwgAAAAAgBIQgwEAAAAASkAM5rBbs2ZNpk+fnvPOOy+XXXZZtm/f3u2cV155JbNmzcqUKVMyefLkPPjgg12PPf/882lpacnUqVPT3NycJ554ojeXDwdV7Vw/9thj+fKXv5zp06enubk59913X28uH95WtbO93z//+c+ce+65aW9v741lwzuqdq537tyZb37zm13f45577unN5cNBVTvXf/3rX3PRRRdl+vTpmTZtWn71q1/15vLhoA5lrvf785//nPHjxx9wzLUjfVW1s+36kR5VwGH00ksvFRMmTCiefvrpoiiKYunSpcXll1/e7byvf/3rxU033VQURVE8//zzxZlnnlls2bKlKIqimDlzZnHXXXcVRVEU69evL84666zi9ddf76UdQHfVzvWuXbuK8ePHF48//nhRFEXxwgsvFBMmTCg2btzYe5uAg+iJn9lFURR79+4tWltbiwkTJhT33Xdf7ywe3kZPzPVVV11VLFy4sCiKonjxxReL8ePHF88++2wv7QC664m5vvjii4sf//jHRVEUxdNPP12MHTu22LVrVy/tALo71LnevXt3cdtttxWf/vSni0984hMHPObakb6o2tl2/UhP88lgDqu1a9fmlFNOySmnnJIkufDCC/PII49kx44dXed0dnbmN7/5TS688MIkyQknnJBJkyZl5cqV2bZtW55++unMmDEjSTJmzJicdNJJeeihh3p9L7BftXPd0dGR+fPn55Of/GSSZOTIkTn22GOzZcuW3t8MvEm1s73fLbfckjFjxuTkk0/u3Q3AQVQ71zt37szq1aszZ86cJEljY2NWrFiR4cOH9/5m4L964uf13r17869//StJ8u9//zsDBw7s5V3AgQ5lrpPk8ccfz3PPPZe2trYDjrt2pK+qdrZdP9LTxGAOqxdffDHHH39819dHHnlkjj766GzdurXr2CuvvJI33ngjxx13XNex4447Llu3bs3WrVszfPjwHHHEEV2PNTY2+qFHTVU718OGDcv555/fdfyuu+7Ka6+9ljPOOKN3NgBvo9rZTpIHH3wwTz75ZL72ta/13sLhHVQ713//+99z9NFH584778xFF12UGTNm5I9//GOGDBnSq/uAN+uJn9cLFizIkiVL8pnPfCYXX3xx5s+fLwhTU4cy10nyqU99Kt/73vfS2Nh4wHHXjvRV1c6260d6WkOtF0B9K4oilUql2/F+/fodcE6Sbuf169fvkJ4Pva3auX7zOT/4wQ9y11135fbbbxcWqLlqZ/tvf/tbbrnllvzoRz/yc5o+o9q53rNnT7Zv356GhoYsX748mzdvzkUXXZQTTjjBRRg1U+1cd3R05Oqrr851112XyZMnZ+PGjbnssssyatSojB079vAuHt5Gtdd+rh3pq3pqNl0/0lP8VOSwGjlyZLZt29b19e7du/PKK69k5MiRXceOPfbYDBgw4IAbqG/bti3HH398Ro4cmX/84x/p7Ozsemz79u0H/F816G3VznWSvP7667nqqqvy61//OnfffXdGjx7dexuAt1HtbP/85z/PG2+8kUsvvTTTp0/Pk08+mZtvvjlLlizpzW3AAaqd6/2fzvniF7+Y5D+/aj9+/Pj84Q9/6KUdQHfVzvXGjRuzc+fOTJ48OUly8sknZ8KECXn00Ud7bxPwFocy1+/2fNeO9EXVznbi+pGeJQZzWJ111lnZsGFDNm7cmCS5++67c9ppp+WYY47pOqd///4599xzc+eddyZJXnjhhaxZsyaf//zn09jYmNGjR+dnP/tZkmTDhg35y1/+kjPPPLP3NwP/Ve1cd3Z25oorrsgRRxyR5cuXe4NKn1HtbM+ZMye//OUv097envb29owZMyZXX311Lr300lpsB5JUP9cnnHBCxowZk5/+9KdJkpdffjm///3vc9ppp/X+ZuC/qp3rk046Kbt37866deuSJDt27MgTTzxhrqmpQ5nrd+Lakb6q2tl2/UhPqxT7f38IDpN169blxhtvTEdHR4499tjccMMN+fCHP5zp06dn4cKFGTt2bF5++eUsWLAgzz77bDo7OzN79uw0NzcnSZ5//vl85zvfyUsvvZQk+da3vuU/6NRcNXP9i1/8InPmzMmoUaMOuKfZnDlzcu6559ZwV1D9z+w39w0w4wAAAMJJREFUa2lpycyZMzN9+vQa7AT+X7Vz/eKLL+a6667Lc889l87Ozlx88cVpaWmp8a4ou2rn+rHHHsv3v//97Nq1K5VKJZdccklmzpxZ411Rdocy1/tt3rw5X/jCF7Jhw4auY64d6auqmW3Xj/Q0MRgAAAAAoATcJgIAAAAAoATEYAAAAACAEhCDAQAAAABKQAwGAAAAACgBMRgAAAAAoATEYAAAAACAEhCDAQAAAABKQAwGAAAAACgBMRgAAAAAoAT+D6bCAyW9ZPIPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x1760 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "X_Reduced = X[['odor_n','odor_f', 'gill-size_n','gill-size_b']]\n",
    "X_Reduced = StandardScaler().fit_transform(X_Reduced)\n",
    "X_Train2, X_Test2, Y_Train2, Y_Test2 = train_test_split(X_Reduced, Y, test_size = 0.30, random_state = 101)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train2,Y_Train2)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test2)\n",
    "print(confusion_matrix(Y_Test2,predictionforest))\n",
    "print(classification_report(Y_Test2,predictionforest))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.3102470000000022\n",
      "[[1248   26]\n",
      " [  53 1111]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97      1274\n",
      "           1       0.98      0.95      0.97      1164\n",
      "\n",
      "    accuracy                           0.97      2438\n",
      "   macro avg       0.97      0.97      0.97      2438\n",
      "weighted avg       0.97      0.97      0.97      2438\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Recursive Feature Elimination"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Recursive Feature Elimination (RFE) takes as input the instance of a Machine Learning model and the final desired number of features to use. It then recursively reduces the number of features to use by ranking them using the Machine Learning model accuracy as metrics. Creating a for loop in which the number of input features is our variable, it could then be possible to found out the optimal number of features our model needs by keeping track of the accuracy registred in each loop iteration. Using RFE support_ method, we can then find out the names of the features which have been evaluated as most important (rfe.support_ return a boolean list in which TRUE represent that a feature is considered as important and FALSE represent that a feature is not considered important).  "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100)\n",
    "rfe = RFE(model, 4)\n",
    "start = time.process_time()\n",
    "RFE_X_Train = rfe.fit_transform(X_Train,Y_Train)\n",
    "RFE_X_Test = rfe.transform(X_Test)\n",
    "rfe = rfe.fit(RFE_X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "print(\"Overall Accuracy using RFE: \", rfe.score(RFE_X_Test,Y_Test))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "28.500087\n",
      "Overall Accuracy using RFE:  0.9753896636587367\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "model = RandomForestClassifier(n_estimators=100)\n",
    "rfe = RFE(model, 4)\n",
    "RFE_X_Train = rfe.fit_transform(X_Train,Y_Train)\n",
    "model.fit(RFE_X_Train,Y_Train) \n",
    "print(\"Number of Features: \", rfe.n_features_)\n",
    "print(\"Selected Features: \")\n",
    "colcheck = pd.Series(rfe.support_,index = list(X.columns))\n",
    "colcheck[colcheck == True].index"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of Features:  4\n",
      "Selected Features: \n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['odor_f', 'odor_n', 'gill-size_n', 'stalk-surface-above-ring_k'], dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### SelectFromModel: Meta-transformer for selecting features based on importance weights."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "SelectFromModel is another Scikit-learn method which can be used for Feature Selection. This method can be used with all the different types of Scikit-learn models (after fitting) which have a coef_ or feature_importances_ attribute. Compared to RFE, SelectFromModel is a less robust solution. In fact, SelectFromModel just removes less important features based on a calculated threshold (no optimization iteration process involved). <br>\n",
    "\n",
    "In order to test SelectFromModel efficacy, I decided to use an ExtraTreesClassifier in this example. ExtraTreesClassifier (Extremely Randomized Trees) is tree based ensamble classifier which can yield less variance compared to Random Forest methods (reducing therefore the risk of overfitting). The main difference between Random Forest and Extremely Randomized Trees is that in Extremely Randomized Trees nodes are sampled without replacement."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "model = ExtraTreesClassifier()\n",
    "start = time.process_time()\n",
    "model = model.fit(X_Train,Y_Train)\n",
    "model = SelectFromModel(model, prefit=True)\n",
    "print(time.process_time() - start)\n",
    "Selected_X = model.transform(X_Train)\n",
    "Selected_X.shape"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.051919999999995525\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/guchen/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(5686, 18)"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(Selected_X, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "Selected_X_Test = model.transform(X_Test)\n",
    "predictionforest = trainedforest.predict(Selected_X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.48377499999998\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "# https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
    "importances = trainedforest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in trainedforest.estimators_], axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(Selected_X.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(Selected_X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(Selected_X.shape[1]), indices)\n",
    "plt.xlim([-1, Selected_X.shape[1]])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Feature ranking:\n",
      "1. feature 4 (0.235408)\n",
      "2. feature 3 (0.136618)\n",
      "3. feature 7 (0.094681)\n",
      "4. feature 6 (0.090673)\n",
      "5. feature 10 (0.083036)\n",
      "6. feature 14 (0.063449)\n",
      "7. feature 1 (0.045488)\n",
      "8. feature 11 (0.042355)\n",
      "9. feature 13 (0.037984)\n",
      "10. feature 16 (0.030843)\n",
      "11. feature 15 (0.026515)\n",
      "12. feature 5 (0.018927)\n",
      "13. feature 9 (0.017914)\n",
      "14. feature 12 (0.017899)\n",
      "15. feature 8 (0.017232)\n",
      "16. feature 2 (0.015643)\n",
      "17. feature 17 (0.015269)\n",
      "18. feature 0 (0.010065)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Correlation Matrix Analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Using Seaborn, we can now plot the Pearson correlation heatmap of our dataset. Inspecting this plot, we can then be able to see the correlation of our independent variables (X) with our label (Y). Finally, we can then select just the features which are most correlated with Y and train/test an SVM model to test the results of this approach."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Using Pearson correlation our returned coefficient values will vary between -1 and 1:\n",
    "- If the correlation between two features is 0 this means that changing any of these two features will not affect the other.\n",
    "- If the correlation between two features is greater than 0 this means that increating the values in one feature will make increase also the values in the other feature (the closer the correlation coefficient is to 1 and the stronger is going to be this bond between the two different features). \n",
    "- If the correlation between two features is less than 0 this means that increating the values in one feature will make decrease the values in the other feature (the closer the correlation coefficient is to -1 and the stronger is going to be this relationship between the two different features). "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    " Another possible aspect to control in this analysis would be to check if the selected variables are highly correlated each other. If they are, we would then need to keep just one of the correlated ones and drop the others."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "Numeric_df = pd.DataFrame(X)\n",
    "Numeric_df['Y'] = Y\n",
    "Numeric_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape_b</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_f</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>cap-surface_y</th>\n",
       "      <th>cap-color_b</th>\n",
       "      <th>cap-color_c</th>\n",
       "      <th>cap-color_e</th>\n",
       "      <th>cap-color_g</th>\n",
       "      <th>cap-color_n</th>\n",
       "      <th>cap-color_p</th>\n",
       "      <th>cap-color_r</th>\n",
       "      <th>cap-color_u</th>\n",
       "      <th>cap-color_w</th>\n",
       "      <th>cap-color_y</th>\n",
       "      <th>bruises_f</th>\n",
       "      <th>bruises_t</th>\n",
       "      <th>odor_a</th>\n",
       "      <th>odor_c</th>\n",
       "      <th>odor_f</th>\n",
       "      <th>odor_l</th>\n",
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       "      <th>odor_p</th>\n",
       "      <th>odor_s</th>\n",
       "      <th>odor_y</th>\n",
       "      <th>gill-attachment_a</th>\n",
       "      <th>gill-attachment_f</th>\n",
       "      <th>gill-spacing_c</th>\n",
       "      <th>gill-spacing_w</th>\n",
       "      <th>gill-size_b</th>\n",
       "      <th>gill-size_n</th>\n",
       "      <th>gill-color_b</th>\n",
       "      <th>gill-color_e</th>\n",
       "      <th>gill-color_g</th>\n",
       "      <th>gill-color_h</th>\n",
       "      <th>gill-color_k</th>\n",
       "      <th>gill-color_n</th>\n",
       "      <th>gill-color_o</th>\n",
       "      <th>gill-color_p</th>\n",
       "      <th>gill-color_r</th>\n",
       "      <th>gill-color_u</th>\n",
       "      <th>gill-color_w</th>\n",
       "      <th>gill-color_y</th>\n",
       "      <th>stalk-shape_e</th>\n",
       "      <th>stalk-shape_t</th>\n",
       "      <th>stalk-root_?</th>\n",
       "      <th>stalk-root_b</th>\n",
       "      <th>stalk-root_c</th>\n",
       "      <th>stalk-root_e</th>\n",
       "      <th>stalk-root_r</th>\n",
       "      <th>stalk-surface-above-ring_f</th>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <th>stalk-surface-above-ring_s</th>\n",
       "      <th>stalk-surface-above-ring_y</th>\n",
       "      <th>stalk-surface-below-ring_f</th>\n",
       "      <th>stalk-surface-below-ring_k</th>\n",
       "      <th>stalk-surface-below-ring_s</th>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <th>stalk-color-above-ring_b</th>\n",
       "      <th>stalk-color-above-ring_c</th>\n",
       "      <th>stalk-color-above-ring_e</th>\n",
       "      <th>stalk-color-above-ring_g</th>\n",
       "      <th>stalk-color-above-ring_n</th>\n",
       "      <th>stalk-color-above-ring_o</th>\n",
       "      <th>stalk-color-above-ring_p</th>\n",
       "      <th>stalk-color-above-ring_w</th>\n",
       "      <th>stalk-color-above-ring_y</th>\n",
       "      <th>stalk-color-below-ring_b</th>\n",
       "      <th>stalk-color-below-ring_c</th>\n",
       "      <th>stalk-color-below-ring_e</th>\n",
       "      <th>stalk-color-below-ring_g</th>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <th>stalk-color-below-ring_o</th>\n",
       "      <th>stalk-color-below-ring_p</th>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <th>stalk-color-below-ring_y</th>\n",
       "      <th>veil-type_p</th>\n",
       "      <th>veil-color_n</th>\n",
       "      <th>veil-color_o</th>\n",
       "      <th>veil-color_w</th>\n",
       "      <th>veil-color_y</th>\n",
       "      <th>ring-number_n</th>\n",
       "      <th>ring-number_o</th>\n",
       "      <th>ring-number_t</th>\n",
       "      <th>ring-type_e</th>\n",
       "      <th>ring-type_f</th>\n",
       "      <th>ring-type_l</th>\n",
       "      <th>ring-type_n</th>\n",
       "      <th>ring-type_p</th>\n",
       "      <th>spore-print-color_b</th>\n",
       "      <th>spore-print-color_h</th>\n",
       "      <th>spore-print-color_k</th>\n",
       "      <th>spore-print-color_n</th>\n",
       "      <th>spore-print-color_o</th>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <th>spore-print-color_u</th>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <th>spore-print-color_y</th>\n",
       "      <th>population_a</th>\n",
       "      <th>population_c</th>\n",
       "      <th>population_n</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_d</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
       "0            1              0              0              1              0   \n",
       "1            1              0              0              1              0   \n",
       "2            0              0              0              1              0   \n",
       "3            1              0              0              0              1   \n",
       "4            1              0              0              1              0   \n",
       "\n",
       "   cap-color_b  cap-color_c  cap-color_e  cap-color_g  cap-color_n  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            1            0   \n",
       "\n",
       "   cap-color_p  cap-color_r  cap-color_u  cap-color_w  cap-color_y  bruises_f  \\\n",
       "0            0            0            0            0            0          0   \n",
       "1            0            0            0            0            1          0   \n",
       "2            0            0            0            1            0          0   \n",
       "3            0            0            0            1            0          0   \n",
       "4            0            0            0            0            0          1   \n",
       "\n",
       "   bruises_t  odor_a  odor_c  odor_f  odor_l  odor_m  odor_n  odor_p  odor_s  \\\n",
       "0          1       0       0       0       0       0       0       1       0   \n",
       "1          1       1       0       0       0       0       0       0       0   \n",
       "2          1       0       0       0       1       0       0       0       0   \n",
       "3          1       0       0       0       0       0       0       1       0   \n",
       "4          0       0       0       0       0       0       1       0       0   \n",
       "\n",
       "   odor_y  gill-attachment_a  gill-attachment_f  gill-spacing_c  \\\n",
       "0       0                  0                  1               1   \n",
       "1       0                  0                  1               1   \n",
       "2       0                  0                  1               1   \n",
       "3       0                  0                  1               1   \n",
       "4       0                  0                  1               0   \n",
       "\n",
       "   gill-spacing_w  gill-size_b  gill-size_n  gill-color_b  gill-color_e  \\\n",
       "0               0            0            1             0             0   \n",
       "1               0            1            0             0             0   \n",
       "2               0            1            0             0             0   \n",
       "3               0            0            1             0             0   \n",
       "4               1            1            0             0             0   \n",
       "\n",
       "   gill-color_g  gill-color_h  gill-color_k  gill-color_n  gill-color_o  \\\n",
       "0             0             0             1             0             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             0             1             0   \n",
       "3             0             0             0             1             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   gill-color_p  gill-color_r  gill-color_u  gill-color_w  gill-color_y  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   stalk-shape_e  stalk-shape_t  stalk-root_?  stalk-root_b  stalk-root_c  \\\n",
       "0              1              0             0             0             0   \n",
       "1              1              0             0             0             1   \n",
       "2              1              0             0             0             1   \n",
       "3              1              0             0             0             0   \n",
       "4              0              1             0             0             0   \n",
       "\n",
       "   stalk-root_e  stalk-root_r  stalk-surface-above-ring_f  \\\n",
       "0             1             0                           0   \n",
       "1             0             0                           0   \n",
       "2             0             0                           0   \n",
       "3             1             0                           0   \n",
       "4             1             0                           0   \n",
       "\n",
       "   stalk-surface-above-ring_k  stalk-surface-above-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-above-ring_y  stalk-surface-below-ring_f  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   stalk-surface-below-ring_k  stalk-surface-below-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-below-ring_y  stalk-color-above-ring_b  \\\n",
       "0                           0                         0   \n",
       "1                           0                         0   \n",
       "2                           0                         0   \n",
       "3                           0                         0   \n",
       "4                           0                         0   \n",
       "\n",
       "   stalk-color-above-ring_c  stalk-color-above-ring_e  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_g  stalk-color-above-ring_n  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_o  stalk-color-above-ring_p  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_w  stalk-color-above-ring_y  \\\n",
       "0                         1                         0   \n",
       "1                         1                         0   \n",
       "2                         1                         0   \n",
       "3                         1                         0   \n",
       "4                         1                         0   \n",
       "\n",
       "   stalk-color-below-ring_b  stalk-color-below-ring_c  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_e  stalk-color-below-ring_g  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_n  stalk-color-below-ring_o  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_p  stalk-color-below-ring_w  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   stalk-color-below-ring_y  veil-type_p  veil-color_n  veil-color_o  \\\n",
       "0                         0            1             0             0   \n",
       "1                         0            1             0             0   \n",
       "2                         0            1             0             0   \n",
       "3                         0            1             0             0   \n",
       "4                         0            1             0             0   \n",
       "\n",
       "   veil-color_w  veil-color_y  ring-number_n  ring-number_o  ring-number_t  \\\n",
       "0             1             0              0              1              0   \n",
       "1             1             0              0              1              0   \n",
       "2             1             0              0              1              0   \n",
       "3             1             0              0              1              0   \n",
       "4             1             0              0              1              0   \n",
       "\n",
       "   ring-type_e  ring-type_f  ring-type_l  ring-type_n  ring-type_p  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            1   \n",
       "2            0            0            0            0            1   \n",
       "3            0            0            0            0            1   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   spore-print-color_b  spore-print-color_h  spore-print-color_k  \\\n",
       "0                    0                    0                    1   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    1   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   spore-print-color_n  spore-print-color_o  spore-print-color_r  \\\n",
       "0                    0                    0                    0   \n",
       "1                    1                    0                    0   \n",
       "2                    1                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   spore-print-color_u  spore-print-color_w  spore-print-color_y  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   population_a  population_c  population_n  population_s  population_v  \\\n",
       "0             0             0             0             1             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             1             0             0   \n",
       "3             0             0             0             1             0   \n",
       "4             1             0             0             0             0   \n",
       "\n",
       "   population_y  habitat_d  habitat_g  habitat_l  habitat_m  habitat_p  \\\n",
       "0             0          0          0          0          0          0   \n",
       "1             0          0          1          0          0          0   \n",
       "2             0          0          0          0          1          0   \n",
       "3             0          0          0          0          0          0   \n",
       "4             0          0          1          0          0          0   \n",
       "\n",
       "   habitat_u  habitat_w  Y  \n",
       "0          1          0  1  \n",
       "1          0          0  0  \n",
       "2          0          0  0  \n",
       "3          1          0  1  \n",
       "4          0          0  0  "
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "corr= Numeric_df.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)\n",
    "\n",
    "# Selecting only correlated features\n",
    "corr_y = abs(corr[\"Y\"])\n",
    "highest_corr = corr_y[corr_y >0.5]\n",
    "highest_corr.sort_values(ascending=True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "bruises_f                     0.501530\n",
       "bruises_t                     0.501530\n",
       "gill-color_b                  0.538808\n",
       "gill-size_b                   0.540024\n",
       "gill-size_n                   0.540024\n",
       "ring-type_p                   0.540469\n",
       "stalk-surface-below-ring_k    0.573524\n",
       "stalk-surface-above-ring_k    0.587658\n",
       "odor_f                        0.623842\n",
       "odor_n                        0.785557\n",
       "Y                             1.000000\n",
       "Name: Y, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 39
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x800 with 2 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "X_Reduced2 = X[['bruises_f' , 'bruises_t' , 'gill-color_b' , 'gill-size_b' , 'gill-size_n' , 'ring-type_p' , 'stalk-surface-below-ring_k' , 'stalk-surface-above-ring_k' , \n",
    "                'odor_f', 'odor_n']]\n",
    "X_Reduced2 = StandardScaler().fit_transform(X_Reduced2)\n",
    "X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_Reduced2, Y, test_size = 0.30, random_state = 101)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "start = time.process_time()\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train3, Y_Train3)\n",
    "print(time.process_time() - start)\n",
    "predictionsvm = trainedsvm.predict(X_Test3)\n",
    "print(confusion_matrix(Y_Test3,predictionsvm))\n",
    "print(classification_report(Y_Test3,predictionsvm))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.04801500000000658\n",
      "[[1248   26]\n",
      " [  46 1118]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97      1274\n",
      "           1       0.98      0.96      0.97      1164\n",
      "\n",
      "    accuracy                           0.97      2438\n",
      "   macro avg       0.97      0.97      0.97      2438\n",
      "weighted avg       0.97      0.97      0.97      2438\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Univariate Feature Selection"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Univariate Feature Selection is a statistical method used to select the features which have the strongest relationship with our corrispondent labels. Using the **SelectKBest** method we can decide which metrics to use to evaluate our features and the number of K best features we want to keep. Different types of scoring functions are available depending on our needs: \n",
    "- Classification: chi2, f_classif, mutual_info_classif\n",
    "- Regression: f_regression, mutual_info_regression\n",
    "\n",
    "In this example, we will be using chi2. [Chi-squared (Chi2)](https://en.wikipedia.org/wiki/Chi-squared_test) can take as input just non-negative values, therefore, first of all we scale our input data in a range between 0 and 1."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "Scaled_X = min_max_scaler.fit_transform(X2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "X_new = SelectKBest(chi2, k=2).fit_transform(Scaled_X, Y)\n",
    "X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_new, Y, test_size = 0.30, random_state = 101)\n",
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train3,Y_Train3)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test3)\n",
    "print(confusion_matrix(Y_Test3,predictionforest))\n",
    "print(classification_report(Y_Test3,predictionforest))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.4762520000000023\n",
      "[[1015  259]\n",
      " [  41 1123]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.80      0.87      1274\n",
      "           1       0.81      0.96      0.88      1164\n",
      "\n",
      "    accuracy                           0.88      2438\n",
      "   macro avg       0.89      0.88      0.88      2438\n",
      "weighted avg       0.89      0.88      0.88      2438\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Lasso Regression"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "When applying regularization to a Machine Learning model, we add a penalty to the model parameters so that to avoid that our model tries to resemble too closely our input data. In this way, we can make our model less complex and we can avoid overfitting (making learn to our model not just the key data characheteristics but also it's intrinsic noise). <br>\n",
    "\n",
    "One of the possible Regularization Methods is Lasso (L1) Regrssion. When using Lasso Regression, the coefficients of the inputs features gets shrinken if they are not positively contributing towards our Machine Learning model training. In this way, some of the features might get automatically discarded assigning them coefficients equal to zero.  "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "regr = LassoCV(cv=5, random_state=101)\n",
    "regr.fit(X_Train,Y_Train)\n",
    "print(\"LassoCV Best Alpha Scored: \", regr.alpha_)\n",
    "print(\"LassoCV Model Accuracy: \", regr.score(X_Test, Y_Test))\n",
    "model_coef = pd.Series(regr.coef_, index = list(X.columns[:-1]))\n",
    "print(\"Variables Eliminated: \", str(sum(model_coef == 0)))\n",
    "print(\"Variables Kept: \", str(sum(model_coef != 0))) "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "LassoCV Best Alpha Scored:  0.0003964898084478826\n",
      "LassoCV Model Accuracy:  0.9971840741918596\n",
      "Variables Eliminated:  73\n",
      "Variables Kept:  44\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/guchen/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.37800350585681874, tolerance: 0.1420043615898695\n",
      "  positive)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "top_coef = model_coef.sort_values()\n",
    "top_coef[top_coef != 0].plot(kind = \"barh\")\n",
    "plt.title(\"Most Important Features Identified using Lasso (!0)\")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most Important Features Identified using Lasso (!0)')"
      ]
     },
     "metadata": {},
     "execution_count": 45
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x800 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit ('base': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "interpreter": {
   "hash": "1fef96a86254b5b64b7294805a674893d583399788ea545149c5bfbe00efcc65"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}